Measuring variability in phytotoxicity testing using crop and wild plant species
Bibliographic record
Abstract
A series of experiments was conducted to assess the level of variability in phytotoxicity testing and to investigate factors that may explain some of the observed uncertainties and inconsistencies. The work was conducted in greenhouse or growth chamber environments with plants growing individually in pots and harvested 28 d after spraying with two herbicides, glyphosate and atrazine, as formulated products. Between six and 10 doses were used on five or six replicates, necessitating over 4,500 individually growing plants. In the first set of experiments, several ecotypes (originating from different areas of the world) of eight wild plant species were tested. Significant differences in sensitivity to atrazine and glyphosate were found among ecotypes of most species tested. In the second suite of experiments, the reproducibility of results during different seasons (when growing conditions vary) was investigated using three crops and four wild plant species. Results showed that seasonal variability elicited a pronounced discrepancy in response between plants tested at different times of the year. It was found that no consistent effects could be attributed to the biotic or abiotic factors investigated. Several ecotypes of the same species differed in their seed size, percentage germination, or germination requirements, as well as in growth patterns, but these differences could not explain differences in herbicide sensitivity. Likewise, differences in phytotoxicity could not be attributed to factors such as temperature, light intensity, and sunlight duration. The present study supports the inclusion of an uncertainty factor in risk assessments to account for the intrinsic variability in plant sensitivity to herbicides.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".