Microbial pesticides II [data evaluation and \nenvironmental risk assessment: a desk study]
Bibliographic record
Abstract
A 1998 study discusses the (im)possibilities of a more structured and transparent system for assessing the environmental risks of microbial pesticides. As current environmental risk assessments by registration authorities are on a case-by-case basis, it is a challenge to transform the existing experience - most of it gained in Canada and the US - into a more systematic approach. A concurrent advantage of such a framework for risk assessment - with the prerequisite of a transparent rationale - may be the acceleration of registration procedures for such pesticides. Major impediments for the development of such a framework are: I the diversity and complexity of microbial organisms and their behaviour and interactions in the environment; II the lack of experience with in situ behaviour, fate, and adverse effects of pesticidal microorganisms to biota (apart from some work with Bacillus thuringiensis and several Baculoviridae); III small market shares to be expected in the near future for new microbial pesticides. Therefore, the present study successively stipulates for microbial pesticides general aspects of identification of hazards, exposure and effects assessment, and risk characterisation. As the identification of hazards includes the evaluation of individual tests, key items are listed and annotated for tests on distribution and fate. This is also done for effects on organisms in the environment. These lists can be used to screen for the purpose of summarising and evaluating experimental tests for pesticide registration.
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.
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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".