What the habitat amount hypothesis does and does not predict: A reply to Saura
Bibliographic record
Abstract
Abstract Saura (2020) used the habitat amount hypothesis (HAH) to predict negative effects of fragmentation per se on mean species density per site over a region. This prediction is valid but incomplete; the HAH can also predict positive effects of fragmentation on mean species density per site over a region. Saura also stated, "the HAH is compatible with a steeper slope of the species–area relationship for fragmented than for continuous habitat, and with higher species richness for a single large patch than for several small patches with the same total area (SLOSS)." Importantly, the HAH does not predict species‐area relationship (SAR) slopes or SLOSS. These require information about how species composition changes over space, while the HAH only predicts species density per site. The HAH is therefore equally compatible with a steeper or shallower SAR slope for fragmented than continuous habitat, and the HAH is equally compatible with either outcome of SLOSS.
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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 | 0.015 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.003 | 0.012 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.019 | 0.030 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".