The filament determination depends on the tracer: comparing filaments based on dark matter particles and galaxies in the <scp>gaea</scp> semi-analytical model
Bibliographic record
Abstract
ABSTRACT Filaments are elongated structures that connect groups and clusters of galaxies and are visually the striking feature in cosmological maps. In the literature, typically filaments are defined only using galaxies, assuming that these are good tracers of the dark matter distribution, despite the fact that galaxies are a biased indicator. Here, we apply the topological filament extractor disperse to the predictions of the semi-analytical code GAlaxy Evolution and Assembly to investigate the correspondence between the properties of z = 0 filaments extracted using the distribution of dark matter and the distribution of model galaxies evolving within the same large-scale structure. We focus on filaments around massive clusters with a mass comparable to Virgo and Coma, with the intent of investigating the influence of massive systems and their feeding filamentary structure on the physical properties of galaxies. We apply different methods to compare the properties of filaments based on the different tracers and study how the sample selection impacts the extraction. Overall, filaments extracted using different tracers agree, although they never coincide totally. We also find that the number of filaments ending up in the massive clusters identified using galaxy distribution is typically underestimated with respect to the corresponding dark matter filament extraction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".