Blow Flies from Forest Fragments Embedded in Different Land Uses: Implications for Selecting Indicators in Forensic Entomology
Bibliographic record
Abstract
Given the general expectation that forest loss can alter biodiversity patterns, we hypothesize that blow fly species abundances differ in a gradient of native vegetation cover. This study was conducted in 17 fragments across different landscapes in central Brazil. Different land cover type proportions were used to represent landscape structure. In total, 2334 specimens of nine species of Calliphoridae were collected. We used principal component analysis (PCA) to reduce dimensionality and multicollinearity of the landscape data. The first component explained 70%, and it represented a gradient of forest-pasture land uses. Alien species showed a wide distribution in different fragments with no clear relationship between the abundance values and the scores of PCA axes, whereas native species occurred only in areas with a predominance of forest cover. Our study revealed that certain native species may be sensitive to forest loss at the landscape scale, and they represent a bioindicator in forensic entomology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.000 |
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 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".