Data used to apply the three-step strategy for predicting algae growth inhibition toxicity [1].
Bibliographic record
Abstract
Table I lists the names of the 309 chemicals selected from the data set of 425 chemicals in Kusk et al. [2], along with the SMILES strings used to calculate their physicochemical properties (log P, log D at pH10, log S0, and log Sw) by means of ACD/Labs [3]. Tables II, III, and IV list log (1/48h-algal EC50 [mM]) values, physicochemical properties, and values used for Steps 1–3 of the three-step strategy [1]; values are from the algal toxicity data set of Kusk et al. [2]. Log P: log of octanol–water partition coefficient, calculated with ACD/Labs using Consensus LogP values. Log D at pH 10: log of octanol–water distribution coefficient at pH 10 (log DpH10 in Tables II, III, and IV), calculated with ACD/Labs using the Consensus LogP and GALAS pKa modules. Log S0: log of intrinsic solubility (i.e., solubility of the neutral form [mol L−1]) in water at 25 °C, calculated with the GALAS algorithm in ACD/Labs. Log Sw: log of solubility in pure water along with pH of the resulting solution (mol L−1 with a defined pH), calculated with the GALAS algorithm in ACD/Labs. Values of log (1/48h-algal EC50 [mM]) were calculated by converting EC50 values in milligrams per liter to the corresponding millimolar values by using the molecular weights listed in the supplemental data of ref. [2]. [1] Furuhama A, Hasunuma K, Hayashi TI, Tatarazako N (2016) Predicting algal growth inhibition toxicity: three-step strategy using structural and physicochem-ical properties. SAR QSAR Environ Res 27:343–362. [2] Kusk KO, Christensen AM, Nyholm N (2018) Algal growth inhibition test re-sults of 425 organic chemical substances. Chemosphere 204:405–412. [3] ACD/Labs, version 2018 (2018). Advanced Chemistry Development, Inc., To-ronto, ON, Canada.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.021 |
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".