Bibliographic record
Abstract
The terms ‘habitat’, ‘environment’ and ‘niche’ are used inconsistently, and with some confusion, within the ecological literature on species distribution and abundance modelling. Here I suggest interrelated working definitions of these terms whereby the concept of habitat remains associated with descriptive/correlative analyses of the environments of organisms, while the niche concept is reserved for mechanistic analyses. To model the niche mechanistically, it is necessary to understand the way an organism's morphology, physiology, and especially behaviour, determine the kinds of environment it experiences when living in a particular habitat, and it is also necessary to understand how those environmental conditions affect fitness (growth, survival and reproduction). While distributions can potentially be predicted by modelling descriptions or correlations between organisms and habitat components, we must model an organism's niche mechanistically if we are to fully explain distribution limits. A mechanistic understanding of the niche is also critical when we want to predict an organism's distribution under novel circumstances such as a species introduction or climate change.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".