Patch‐scale edge effects do not indicate landscape‐scale fragmentation effects
Bibliographic record
Abstract
Abstract Negative landscape‐scale fragmentation effects are often inferred from negative patch‐scale edge effects. I tested this cross‐scale extrapolation using two evaluations. First, I searched for studies that estimated the direction of both a patch‐scale edge effect and a landscape‐scale fragmentation effect. The directions were concordant and discordant in 55% and 45% of cases, respectively. Second, I extracted from the literature a sample of landscape‐scale fragmentation effects on individual species. Then, for each species I searched for studies from which I could calculate the slope of its patch‐scale edge effect. Species showing negative patch‐scale edge effects were nearly equally likely to show negative or positive landscape‐scale fragmentation effects, and likewise for species showing positive patch‐scale edge effects. The results mean that the efficacy of policies related to habitat fragmentation cannot be inferred from observed patch‐scale edge effects. Such policies require landscape‐scale evidence, comparing species' responses in landscapes with different levels of fragmentation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".