Measuring and interpreting trait‐based selection versus meta‐community effects during local community assembly
Bibliographic record
Abstract
Abstract Questions (1) How can one quantify the relative importance of meta‐community processes related to immigration, local trait‐based habitat filtering, and demographic stochasticity using the Community Assembly by Trait Selection (CATS) model in a general context? (2) How can this generalization be used to detect different strengths and directions in trait selection at the meta‐community and local community levels? Methods I describe a decomposition of the deviance between observed and predicted relative abundances based on a maximum entropy model including a meta‐community prior (CATS) and generalize a previous decomposition of relative abundance using this model; corrections to avoid negative explained proportions of deviance are presented. Simulations of community assembly are used to explore its properties and elucidate its interpretation. In particular, this method quantifies the proportion of the total deviance between observed and predicted relative abundances attributed to: (1) pure trait‐based local selection, (2) dispersal mass effect from the meta‐community, (3) joint contributions of (1) and (2) that cannot be separated; and (4) residual deviance due to demographic stochasticity. Results and Conclusions The previous decomposition, while giving correct values in that particular data set, requires modification in order to avoid nonsensical negative values. When the modifications described in this paper are made, the decomposition provides correct values. Furthermore, positive or negative values of the joint composition inform us of the importance and direction of correlations between local trait‐based selection and processes occurring in the larger meta‐community.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".