Unveiling the Hidden Feast: from molecular detection to predation rate – An example on biological control by generalist predators
Bibliographic record
Abstract
Abstract Very few processes are as decisive as predation in shaping the structure and dynamics of ecological communities. For most predator species, the number of prey items killed by a predator in a day (predation rate) remains impossible to assess because direct observations are scarce or impossible to acquire. To fill this gap, we propose here a Hierarchical Bayesian Model that integrates data on the molecular detection of prey in predators (e.g. PCR results), on individuals captured in the field on the one hand, and on individuals fed in the laboratory, on the other, in a novel mechanistic framework. By explicitly combining the processes of predation and digestion, model fit provides an estimate of the slope and intercept of the digestion curve, and an estimate of the number of prey consumed by a predator in a day. In a case study targeting 25 carabid beetle species and 5 types of prey in agricultural fields (winter wheat), we use our model to estimate predation rates at species and community scales and demonstrate its advantages for studies on biocontrol and beyond. Code and Data are available on this repository : /r/HBM_PredationCode-C725/
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".