Preliminary results of a survey on the role of arthropod rearing in classical weed biological control.
Bibliographic record
Abstract
The rearing of arthropods is an essential but sometimes neglected and underestimated part of a classical weed biological control programme. Success in rearing is usually a pre-requisite to conducting host-specificity tests, obtaining enough individuals for initial field release or, later, for large-scale implementation. Although most biological control researchers can list situations where agent development has been stopped or slowed due to rearing difficulties, failures seldom get reported in the literature, thus preventing us from gauging the extent and relevance of rearing issues. To rectify this, a questionnaire was developed to investigate the prevalence of rearing problems in weed biological control programmes and to classify their occurrence according to a list of variables (e.g. taxonomy, biological features, genetic issues and researcher/programme attributes). The questionnaire was sent to 80 researchers from eight countries; 65% responded, generating 79 useful responses. Results confirm that, of the challenges faced in programmes, rearing is the most prevalent (56% out of ten possible general problem categories). The most common rearing problems encountered were conditions that were not conducive to mating and/or oviposition (30% of reported arthropod cases) or development (22% of reported arthropod cases). Our results identify key areas for rearing improvement, thus contributing to increased weed biological control project successes.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".