Continuing Advances in Acute Dietary Risk Assessment for Agrochemicals
Bibliographic record
Abstract
P-637 Abstract: A range of practices currently exist within regulatory agencies worldwide for assessing the risk from human dietary consumption of agrochemicals used for plant protection. For instance, EU Authorities generally conduct dietary risk analyses using deterministic models based on single point residue values. In The United States and Canada regulators conduct probabilistic analyses of acute dietary risk using the range of available residue data and extensive information on dietary consumption patterns. The advantage of conducting probabilistic assessments is that a more complete and realistic understanding of potential risks from several sources of exposure (e.g. multiple food items and water) can be developed providing that reliable databases on consumption patterns and residue values are available. Typically acute risks are assessed from single dose studies assessed against consumption within a 24-hour period. Probabilistic methods, however, can be adapted to refine the risks for rapidly reversible compounds such as carbamates provided that appropriate information on the rate of reversibility and the timing of dietary consumption are available. The probabilistic, risk assessment model CARES® has been adapted to allow the effects of within-day exposure patterns to be assessed based on a minute by minute analysis for both food and drinking water consumption. Separate food and drinking water modules for minute by minute analyses have been developed within the model. The results from single modules can be combined to provide an aggregate assessment. The USDA Continuing Survey of Food Intakes by Individuals contains the appropriate time related data to construct a time series of within-day consumption of foods, not including drinking water. Additional within day drinking water consumption data were collected by Bayer CropScience to create a daily time series of within-day drinking water consumption. These consumption data were matched to an appropriate person in the CARES reference population based on statistical matching methods using demographic characteristics of the sampled individual to describe the consumption of food and drinking water on a minute (vs. 24 hour) basis. A comparison of acute dietary methodologies for assessing potential risks from a carbamate exhibiting rapidly reversible cholinesterase inhibition is presented. The evaluation includes: deterministic methods; probabilistic methods based on an arbitrary timeframe for acute effects (24h); and an assessment where the timeframe is based on the known properties of the compound. The last scenario represents the most appropriate acute dietary analysis using all available data including consumption, residues and duration of effect.
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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 | 0.001 | 0.001 |
| 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".