Does forest fragmentation cause an increase in forest temperature?
Bibliographic record
Abstract
Abstract Forest fragmentation is considered by many to be a primary cause of the current biodiversity crisis. The underlying mechanisms are poorly known, but a potentially important one is associated with altered thermal conditions within the remaining forest patches, especially at forest edges. Yet, large uncertainty remains about the effect of fragmentation on forest temperature, as it is unclear whether temperature decreases from forest edge to forest interior, and whether this local gradient scales up to an effect of fragmentation (landscape attribute) on temperature. We calculated the effect size (correlation coefficient) of distance from forest edge on air temperature, and tested for differences among forest types surrounded by different matrices using meta‐analysis techniques. We found a negative edge‐interior temperature gradient, but correlation coefficients were highly variable, and significant only for temperate and tropical forests surrounded by a highly contrasting open matrix. Nevertheless, it is unclear if these local‐scale changes in temperature can be scaled up to an effect of fragmentation on temperature. Although it may be valid when considering “fragmentation” as forest loss only, the landscape‐scale inference is not so clear when we consider the second aspect of fragmentation, where a given amount of forest is divided into a large number of small patches (fragmentation per se). Therefore, care is needed when assuming that fragmentation changes forest temperature, as thermal changes at forest edges depend on forest type and matrix composition, and it is still uncertain if this local gradient can be scaled up to the landscape.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".