Edge Effects do not Predict Effects of Fragmentation per se on Understory Forest Vegetation
Bibliographic record
Abstract
Habitat loss is the main cause of biodiversity loss.It is often closely associated with fragmentation, i.e. the breaking apart of habitat.Researchers interested in fragmentation effects on species/biodiversity often study processes in habitat patches (e.g.edge effects) and scale-up these effects to make conclusions about fragmentation at the landscape scale.However, such extrapolations could result in erroneous conclusions and incorrect management decisions if there is no (or a weak) relationship between effects of edge and fragmentation on species/biodiversity.Here we separately measured edge effects on understory forest plant species using 72 forest edge-interior transects and fragmentation effects on the same species across 70 forest sites, to test for a cross-species relationship between edge and fragmentation effects.We found that edge effects did not predict forest plant species responses to fragmentation at the landscape scale.Therefore, researchers should be cautious about scaling up from the patch to landscape scale.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".