SLOSS analysis does not show several small patches contain more species than expected
Bibliographic record
Abstract
Overlap in species-accumulation curves ordered from small-large and large-small (aka SLOSS analysis) is an important line of evidence inferring weak positive diversity effects of fragmentation per se. Yet combining patches in small-large order maximises the probability of encountering new species for every patch, invalidating comparison with large-small order. Controlling for this using simulated null communities, I test species accumulation against a random expectation for 201 published datasets from islands, habitat islands and fragments and compare inference using both methods. SLOSS analysis found 67% positive, 7% negative and 26% no response among datasets. Using simulation, analogous values were 4%, 12% and 40% respectively with no clear outcome in the remainder. SLOSS analysis provides unreliable inference on the diversity effects of habitat subdivision. Accumulation of species in small-large patch size order is more likely to result in fewer than expected species than more, with no effect being the most probable result.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".