Adding magnesium to the silver-gill binding model for rainbow trout (<i>Oncorhynchus mykiss</i>)
Bibliographic record
Abstract
Rainbow trout (Oncorhynchus mykiss; 2-17 g) were exposed to approximately 0.1 microM silver as AgNO3 for 3 to 4 h in synthetic, ion-poor water (20 microM Ca, 100 microM Na, 150 microM Cl, pH 7) to which was added Mg, Ca, or thiosulfate (S2O3). Gills were extracted and assayed for Ag using graphite furnace atomic absorption spectrophotometry. Up to 210 mM Mg (fourfold the concentration of Mg in seawater) did not reduce accumulation of Ag by trout gills. The conditional equilibrium stability constant (K) for Mg at silver-binding sites on the gills was calculated to be log K(Mg-gillAg) = 3.0, or approximately half-as-strong binding as for Ca at these sites. The inclusion of the Mg-gill stability constant into the original Ag-gill binding model increases the flexibility of the model, although the competitive effects of Mg are only important in sodium-poor systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".