Additional file 1 of An often-overestimated ecological risk of copper in Chinese surface water: bioavailable fraction determined by multiple linear regression of water quality parameters
Bibliographic record
Abstract
Additional file1: Figure S1. Comparison of measured versus MLR-, hardness-based- and BLM-predicted for 9 species. Figure S2. Comparison of fitted models to derive the HC5 based on MLR models. Table S1. Concentrations of total dissolved Cu in Chinese surface water of fifteen regions in 2021 (μg/L). Table S2. Water quality variables in Chinese surface freshwater of fifteen regions. Table S3. Suggestions of hardness, pH and DOC values for China, compared to previous works. Table S4. Species-specific multiple linear regression model coefficients. Table S5. Species-specific hardness-based model coefficients. Table S6. Raw and normalization of toxicity data for Cu. Table S7. The MOS10 of Cu in surface water of China under different water quality parameters condition. Table S8. Comparison of species-specific and pooled Cu MLR in Species and numbers of toxicity data to previous work. Table S9. Acute copper toxicity data used for normalization models development. Table S10. Hardness data source. Table S11. DOC data source.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.753 | 0.102 |
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".