On the PNN Modeling of Estrogen Receptor Binding Data for Carboxylic Acid Esters and Organochlorine Compounds†
Bibliographic record
Abstract
Abstract We describe the relationship between the estrogen receptor binding and the molecular structure of chemicals using the probabilistic neural network methodology with structural fragment descriptors as input variables and a data set of 1118 compounds. Exploratory models identified two subsets of chemicals for which the predictions were well correlated with the measured values, namely chlorine-containing compounds and carboxylic esters, and for which individual models were developed. Both compound classes are in the classification system for chemicals on the Canadian Domestic Substances List (DSL) and the data cover five orders of magnitude in activity in each of these classes. The results show excellent performance of both models and are highly encouraging in the search for other models for this and other receptor binding data as well as other classes of DSL substances. They also confirm the flexibility, usefulness and applicability of the probabilistic neural networks as modeling methodology to a wide variety of modeling challenges in the environmental and health fields.
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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 | 0.017 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".