Use of nonlinear regression techniques for describing concentration-response relationships of plant species exposed to contaminated site soils
Bibliographic record
Abstract
Abstract The objectives of this study were to examine the effects of two contaminated site soils on seedling emergence and growth, compare the responses of different endpoints and species sensitivity, and develop appropriate statistical methods for the analysis of concentration-response curves. Plants were exposed to field-collected soils contaminated with amines or condensate. We reparameterized three nonlinear models (logistic, logistic with hormesis, and exponential) to determine any inhibiting concentration for a specified percent effect and confidence interval using regression analysis. Weighting procedures were applied, when necessary, to accommodate heteroscedasticity. This nonlinear regression approach was very satisfactory when used with data sets, each with 11 treatments, and produced an accurate, easily interpreted, and quantitative description of the data, which also provided qualitative information. The IC50s ranged from 2 to 96% contamination for condensate-contaminated soil and from 3 to 38% contamination for amine-contaminated soil. The responses were specific to species, endpoint, and soil. Mass measurements were generally more sensitive and precise than length measurements. Definitive tests were more sensitive than acute tests for endpoints other than emergence.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".