The EGIDE project releases a tenfold larger catalogue of edge-on galaxies with griz photometry, stellar masses, redshifts and star formation rates, finding that red-sequence galaxies are thicker than blue-cloud ones and show a mass-dependent increase in flattening ratio.
URL https://www.cambridge.org/core/product/identifier/S303 3373325100410/type/journal_article
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A conceptual framework reframes AI loss of control by anchoring the definition of control to goal setting and alignment, arguing that such loss can occur with existing AI systems.
citing papers explorer
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The Edge-on Galaxies in the DESI survey (EGIDE): sample building and photometry
The EGIDE project releases a tenfold larger catalogue of edge-on galaxies with griz photometry, stellar masses, redshifts and star formation rates, finding that red-sequence galaxies are thicker than blue-cloud ones and show a mass-dependent increase in flattening ratio.
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Reframing AI Loss of Control: What It Is, How to Have It, How to Lose It
A conceptual framework reframes AI loss of control by anchoring the definition of control to goal setting and alignment, arguing that such loss can occur with existing AI systems.