Pith. sign in

Paper Citation Record · LEDGER

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning

As of 12 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2608.08197.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2608.08197 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:20:53.114122Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

15 of 15 outbound references displayed

  • verified exact1
  • verified fuzzy9
  • unresolved5
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 27afd890-250a-423d-bdfb-0e162120d5e2 · outbound

This paper cites Sarawagi, R.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Sarawagi, R

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T00:20:53.362850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.047078Z digest=sha256:853d40636a1baf8c4b313386490360013ca803b7deb98781702c2504c30f2e53

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T00:20:53.349521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.052259Z digest=sha256:8ffc25f3428fdf714a6fc59557dc9ea382309dbdd6272bd415117fc2fa300cec

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T00:20:53.334861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.057109Z digest=sha256:53a06c60c0599c3915bef0654e2366c0c8e6bf72ae1c68ec26e53a107b58a1ef

Observation 703d5e39-fa09-466b-8eff-cbaee91e4092 · outbound

This paper cites Joglekar, H.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Joglekar, H

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T00:20:53.320636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.061729Z digest=sha256:75bd6ec68c0b39f707dcfb027185225253a5e13ecd6ffc965316e4a3dccd89f6

Observation 5ea88ed3-3e77-4ad9-884d-07577d5928fa · outbound

This paper cites an unresolved cited work.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Unresolved cited work

Reference 5

Resolution
unresolved
raw_fallback, observed 2026-08-12T00:20:53.305538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.066537Z digest=sha256:809b20fe4eecaa3237cf3050256db2ebf5e19d4d0acf71cbb8070008dd3c9363

Observation 73c8e9dd-9f7b-4779-9d49-a63e45713645 · outbound

This paper cites Duivesteijn, A.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Duivesteijn, A

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T00:20:53.290714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.070891Z digest=sha256:c08e7d8f1fc274eb4234e03ca41a3778613a4634da1d567d1959939f7903360b

Observation 93f855cf-f10e-41de-852e-2c9eba556b8b · outbound

This paper cites Lemmerich, M.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Lemmerich, M

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T00:20:53.277627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.076222Z digest=sha256:ce88edd6a6174a9a9bffad3aa5192752b820701d1bf295cb64a1ef9f86e71aa1

Observation faf5f0f1-9a53-42d4-9882-583c51e6e56c · outbound

This paper cites Moshkovitz, S.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Moshkovitz, S

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T00:20:53.262860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.082032Z digest=sha256:324a208b9c9731e0cd2623821448c9f53b0a1b3052ccd84de454ee97961426f0

Observation bc50bfe3-893e-40ab-a754-72419a01ebcc · outbound

This paper cites Near-optimal Algorithms for Explainable k-Medians and k-Means.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Near-optimal Algorithms for Explainable k-Medians and k-Means

Reference 9

Resolution
verified exact
local_arxiv, observed 2026-08-12T00:20:53.177445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.088142Z digest=sha256:21c8797ae0050bdd9b2dd3b55488480a44389e7321befd918958eee7a4739316

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-12T00:20:53.092590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:20:53.092590Z digest=sha256:302d76354f1567685b3500204617603556c1c5061f9f977e571a736ece99d333

Observation dec8719a-213f-4665-9f9c-8f0a5ce99f0a · outbound

This paper cites Aunified approachtointerpretingmodelpredictions.InAdvances in Neural Information Processing Systems (NIPS), volume 30, pages 4765–4774, 2017.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Aunified approachtointerpretingmodelpredictions.InAdvances in Neural Information Processing Systems (NIPS), volume 30, pages 4765–4774, 2017

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T00:20:53.248217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.097111Z digest=sha256:22bba4c5b98538af9c8dbfe6a81bc2d14b625fa405d81977d046927dc389b89b

Observation b1876a1f-b3d8-4086-b063-85e2fade3c61 · outbound

This paper cites an unresolved cited work.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Unresolved cited work

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-12T00:20:53.101646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:20:53.101646Z digest=sha256:c0d423e8642811f07830451ff1584c2a4fbd55f5dd17bef0d51ac8b2da38a24c

Observation 1ff89307-e69d-41cf-90bd-1e708f55ec3a · outbound

This paper cites an unresolved cited work.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Unresolved cited work

Reference 13

Resolution
unresolved
raw_fallback, observed 2026-08-12T00:20:53.223404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.105990Z digest=sha256:df19433603037261b120e867dc76a43e86810bf925d674505b65bbba2a26bae1

Observation 38cd509f-2996-454d-a735-8246d427faab · outbound

This paper cites Pedregosa, G.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Pedregosa, G

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T00:20:53.207044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.110216Z digest=sha256:bdb0b26df280a2fc53c471fc2d212f07d8f3f134e27de1c9e21c2ca669e2da6f

Observation e38e2f25-bb22-46b1-ad03-2fbd73b48c15 · outbound

This paper cites an unresolved cited work.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Unresolved cited work

Reference 15

Resolution
unresolved
raw_fallback, observed 2026-08-12T00:20:53.192957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.114122Z digest=sha256:22dfaa195a27818b839363a21aaac522754313a64f9b404eef945fbfbc777920

Pith citing papers

No inbound Pith citation observations are available.