Female Blow Flies As Vertebrate Resource Indicators
Bibliographic record
Abstract
Rapid vertebrate diversity evaluation is invaluable for monitoring changing ecosystems worldwide. Wild blow flies naturally recover DNA and chemical signatures from animal carcasses and feces. We demonstrate the power of blow flies as biodiversity monitors through sampling of flies in three environments with varying human influences: Indianapolis, IN and two national parks (the Great Smoky Mountains and Yellowstone). Dissected fly guts underwent vertebrate DNA sequencing (12S and 16S rRNA genes) and fecal metabolite screening. Integrated Nested Laplace Approximation (INLA) was used to determine the most important abiotic factor influencing fly-derived vertebrate richness. In 720 min total sampling time, 28 vertebrate species were identified, with 42% of flies containing vertebrate resources: 23% DNA, 5% feces, and 14% contained both. The species of blow fly used was not important for vertebrate DNA recovery, however the use of female flies versus male flies directly influenced DNA detection. Temperature was statistically relevant across environments in maximizing vertebrate detection (mean = 0.098, sd = 0.048). This method will empower ecologists to test vertebrate community ecology theories previously out of reach due practical challenges associated with traditional sampling.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".