Bibliographic record
Abstract
ABSTRACT Galaxy cluster mass haloes (‘clusters’) in a dark matter simulation are matched to nodes in several different cosmic webs found using the disperse cosmic web finder. The webs have different simulation smoothings and disperse parameter choices; for each, four methods are considered for matching disperse nodes to clusters. For most of the webs, disperse nodes outnumber clusters, but not every cluster has a disperse node match (and sometimes >1 cluster matches to the same disperse node). The clusters frequently lacking a matching disperse node have a different distribution of local shear trends and perhaps merger histories. It might be interesting to see in what other ways, e.g. observational properties, these clusters differ. For the webs with smoothing ≤ 2.5 Mpc h−1, and all but the most restrictive matching criterion, ∼3/4 of the clusters always have a disperse node counterpart. The nearest cluster to a given disperse node and vice versa, within twice the smoothing length, obey a cluster mass-disperse node density relation. Cluster pairs where both clusters match disperse nodes can also be assigned the filaments between those nodes, but as the web and matching methods are varied most such filaments do not remain. There is an enhancement of subhalo counts and halo mass between cluster pairs, averaging over cluster pairs assigned disperse filaments increases the enhancement. The approach here also lends itself to comparing nodes across many cosmic web constructions, using the fixed underlying cluster distribution to make a correspondence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".