Comparison of a score-based approach with risk-based ranking of in-use agricultural pesticides in canada to aquatic receptors
Bibliographic record
Abstract
Abstract The purpose of this paper is to present a new risk-based approach developed by Environment Canada for ranking pesticides and their potential risk to aquatic life. These rankings are compared to those generated using a more traditional score-based approach. Two hundred and twelve active ingredients registered in Canada for use in agricultural field crops were included in this assessment. For each major aquatic taxon assessed (fish, insects, crustaceans, algae, and macrophytes), risk was calculated by dividing the 96-h estimated environmental concentration (modeled using parameters such as the application rate and method as well as physicochemical properties) by HC5 values (obtained through calculations of species sensitivity distribution — based toxicity endpoints). The traditional approach assigned scores based on toxicity endpoints in standard test species as well as physicochemical properties associated with the potential for aquatic contamination. A number of similarities were observed between the rankings but also notable differences. Only 22 active ingredients were common to the top 50 ranking positions from both approaches. The main reasons that accounted for the discrepancies between both rankings were the choice of toxicity endpoints, whether based on single or multiple species; the taxonomic breadth of retained data; the use of scores versus risk quotients; and the choices made in situations where multiple data points were available. We conclude that a risk-based approach that considers a broad representation of species toxicity data and estimates of runoff and drift concentration in receiving aquatic systems (even from generic application scenarios) is a more realistic representation of potential toxicological effects and a superior method of ranking products for the risk they pose to the aquatic environment.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".