{"id":"0ca42228-baa8-41ec-8df1-646b69e0f1ae","arxiv_id":"2607.02230","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":2.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"Empirical comparison of OvA and OvR strategies with confidence-based human-in-the-loop on a municipal waste classification dataset aligned to Goslar rules.","lead":"The paper evaluates One-vs-All versus One-vs-Rest classifiers on a Goslar-specific waste dataset and tests confidence thresholds to route uncertain predictions to human review. A generalist reader might examine it for practical trade-offs when deploying configurable AI tools to support local recycling rules.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"No significant objection identified","rationale":"The reader's weakest_assumption correctly flags the missing validation of dataset fidelity and confidence-misclassification correlation. Because the supplied text contains only the plan and no supporting data or analysis, no further internal inconsistency or technical flaw can be isolated; the UNVERDICTED status is appropriate.","tokens_in":1734,"tokens_out":215,"duration_ms":12796,"concrete_test":"Locate the results section (or tables/figures) that report misclassification rates and human-review counts at multiple thresholds for both OvA and OvR; verify whether any quantitative comparison or baseline is present.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The abstract describes an intended evaluation of OvA vs OvR using confidence thresholds on a Goslar-aligned dataset, but supplies no methods, results, or validation details. Without those elements the central claim cannot be assessed for correctness; the reader's UNVERDICTED verdict follows directly from the absence of any empirical content to scrutinize.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript evaluates One-vs-All (OvA) and One-vs-Rest (OvR) classification strategies on a dataset constructed to match the waste categories and sorting scheme of Goslar, Germany. It applies varying confidence thresholds to flag uncertain samples for human review in a human-in-the-loop setup, with the goal of balancing the number of misclassifications against the human annotation effort required for effective municipal waste sorting to support the circular economy.","tokens_in":1769,"tokens_out":335,"duration_ms":12817,"significance":"A well-executed empirical comparison that identifies which strategy better trades off error reduction against human review cost on a municipality-specific dataset could inform practical deployment of AI tools for waste sorting. The work's focus on confidence-guided human-in-the-loop is relevant to real-world constraints, but the absence of any reported performance metrics, dataset statistics, error bars, or method details prevents assessment of whether the claimed balance is achieved.","major_comments":[{"comment":"The manuscript contains no empirical results, performance numbers, dataset statistics, or method details (e.g., model architectures, training procedures, or threshold selection), so it is impossible to verify whether either strategy actually achieves the stated balance between misclassifications and human effort.","section":null},{"comment":"No validation is provided that the constructed Goslar-aligned dataset reflects real-world waste distributions or that model confidence scores reliably predict misclassifications, which is load-bearing for the central human-in-the-loop claim.","section":null}],"minor_comments":[],"recommendation":"uncertain","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their detailed review and constructive comments on our manuscript. We address each of the major comments below and outline the revisions we will make to strengthen the paper.","responses":[{"response":"We agree with this observation. The submitted manuscript focuses on describing the evaluation framework and the comparison approach but omits the specific quantitative results and implementation details. This limits the ability to assess the findings. In the revised manuscript, we will add comprehensive empirical results including performance metrics (accuracy, precision, recall for OvA and OvR), dataset statistics (number of samples per class, total size), model architectures used, training procedures, and the method for selecting confidence thresholds. We will also include figures showing the trade-off between misclassifications and human review effort at different thresholds.","revision_made":"yes","referee_comment":"The manuscript contains no empirical results, performance numbers, dataset statistics, or method details (e.g., model architectures, training procedures, or threshold selection), so it is impossible to verify whether either strategy actually achieves the stated balance between misclassifications and human effort."},{"response":"This is a valid point. While the dataset was constructed to align with Goslar's waste categories and sorting scheme, we did not include explicit validation against real-world distributions or calibration analysis for the confidence scores. In the revision, we will expand the dataset section to describe the construction process in more detail, provide any available statistics or sources used for alignment, and add an analysis of the relationship between model confidence and actual misclassification rates to support the human-in-the-loop strategy. We will also discuss limitations regarding the representativeness of the dataset.","revision_made":"yes","referee_comment":"No validation is provided that the constructed Goslar-aligned dataset reflects real-world waste distributions or that model confidence scores reliably predict misclassifications, which is load-bearing for the central human-in-the-loop claim."}],"tokens_in":1331,"tokens_out":419,"duration_ms":19655,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The paper applies standard One-vs-All and One-vs-Rest classifiers with confidence thresholds to flag uncertain samples for human review, using a dataset built to match Goslar's waste categories and sorting rules. The goal is to reduce mis-sorted household waste while keeping human annotation effort reasonable.\n\nIt does a clear job stating the practical problem: German municipalities have different rules, and AI tools need to be configurable to local schemes if they are to help the circular economy. Aligning the labels to one city's actual categories is a concrete, useful choice rather than a generic dataset.\n\nThe soft spot is the total lack of empirical content. The abstract describes the plan and the intended comparison but gives no dataset size, class counts, model details, accuracy figures, or results at different thresholds. There is nothing to show whether either strategy actually reduces misclassifications more than the other or at what human cost. The assumption that model confidence will reliably identify likely errors is stated but not tested against this data.\n\nThis is aimed at engineers or applied groups building waste-sorting apps for specific European cities. A practitioner might borrow the dataset-construction approach or the threshold idea as a starting template. It does not add new methods or general findings that would interest the broader classification or active-learning community.\n\nI would not bring it to a reading group or cite it. It does not look ready for peer review because the central evaluation is missing; the work would need the actual experiments and numbers before a referee could assess whether the claims hold.","headline":"Routine OvA vs OvR comparison on a Goslar waste dataset, but the abstract supplies no results, numbers, or validation so the claimed balance of errors and human effort cannot be checked.","tokens_in":2272,"tokens_out":390,"would_cite":false,"duration_ms":25916,"reading_group":"no","serious_thinker":"unclear","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Confidence thresholds on OvA and OvR models trade off fewer waste-sorting errors against the volume of cases sent for human review.","keywords":["waste sorting","one-vs-all","one-vs-rest","human-in-the-loop","confidence threshold","circular economy","multi-class classification","Germany"],"falsifier":"Run the trained models on a fresh collection of real Goslar household waste photos with ground-truth labels and check whether the error rate below the chosen threshold is substantially higher than the error rate above it.","tokens_in":2635,"feed_emoji":"♻","tokens_out":616,"duration_ms":17912,"temperature":0.7,"pith_summary":"The paper compares One-vs-All and One-vs-Rest classification on images of household waste sorted according to the city of Goslar's rules. It tests whether the models' confidence scores can reliably mark uncertain predictions so those items can be passed to a human annotator instead of being shown to residents. The central goal is to lower the rate of incorrect disposal advice while keeping the human workload within practical limits. If the thresholds work as intended, municipalities gain a practical way to adapt an AI app to their own waste categories without needing a perfect classifier from the start.","feed_headline":"Thresholds tune waste AI accuracy against human review cost","feed_subtitle":"OvA and OvR compared on Goslar dataset to flag uncertain items while cutting overall sorting errors","key_machinery":"Confidence threshold applied to OvA and OvR output scores to select uncertain samples for human-in-the-loop review.","core_discovery":"By training OvA and OvR models on a dataset built to match Goslar's waste categories and then sweeping confidence thresholds, the fraction of misclassified items that reach users can be reduced while the number of samples routed to human review remains controllable.","pith_inferences":["The threshold method could be reused in other image-classification settings where local rules change and some human oversight is available.","If the correlation between confidence and error holds, the human labels collected on uncertain cases can be fed back to retrain the model without labeling the entire stream.","Municipalities could begin with a conservative threshold and raise it over time once resident feedback confirms the model's reliability."],"forward_implications":["Varying the threshold produces explicit accuracy-effort curves for each strategy.","OvA and OvR can differ in how sharply their confidence scores separate correct from incorrect predictions.","The same pipeline can be retrained on any other municipality's category list without changing the human-review logic.","The approach supports incremental deployment: start with a loose threshold and tighten it as more labeled data arrives."],"fun_headline_variants":["OvA and OvR compared with confidence thresholds on Goslar waste","Threshold tuning balances misclassifications and human review","Goslar waste dataset evaluates OvA versus OvR classification","Confidence guided comparison of OvA and OvR for waste sorting"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The dataset matches real Goslar waste items and rules, and lower model confidence scores actually mark items that would be misclassified.","fun_headline_variants_meta":{"raw":{"variants":["OvA and OvR compared with confidence thresholds on Goslar waste","Threshold tuning balances misclassifications and human review","Goslar waste dataset evaluates OvA versus OvR classification","Confidence guided comparison of OvA and OvR for waste sorting"]},"model":"grok-4.3","cost_usd":0.007275,"raw_usage":{"total_tokens":3341,"prompt_tokens":647,"num_sources_used":0,"completion_tokens":60,"cost_in_usd_ticks":72749500,"prompt_tokens_details":{"text_tokens":647,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2634,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":647,"tokens_out":60,"duration_ms":22101,"temperature":1.0,"reasoning_tokens":2634,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-07-03T15:52:04.649490+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Run the trained models on a fresh collection of real Goslar household waste photos with ground-truth labels and check whether the error rate below the chosen threshold is substantially higher than the error rate above it.","supporting_citations":[],"review_version":1}