Preliminary results of a survey on the role of arthropod rearing in classical weed biological control.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it