Application of the biotic ligand model to explain potassium interaction with thallium uptake and toxicity to plankton
Why this work is in the frame
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Bibliographic record
Abstract
Competitive interaction between TI(I) and K was successfully predicted by the biotic ligand model (BLM) for the microalga Chlorella sp. (Chlorophyta; University of Toronto Culture Collection strain 522) during 96-h toxicity tests. Because of a greater affinity of T1(I) (log K = 7.3-7.4) as compared to K (log K = 5.3-6.3) for biologically sensitive sites, an excess of 40- to 160-fold of K is required to suppress T1(I) toxic effects on Chlorella sp., regardless of [T1(I)] in solution. Similar excess of K is required to suppress T1(I) toxicity to Synechococcus leopoliensis (Cyanobacteria; University of Texas Culture Collection strain 625) and Brachionus calyciflorus (Rotifera; strain AB-RIF). The mechanism for the mitigating effect of K on T1(I) toxicity was investigated by measuring 204T1(I) cellular uptake flux and efflux in Chlorella sp. Potassium shows a competitive effect on T1(I) uptake fluxes that could be modeled using the BLM-derived stability constants and a Michaelis-Menten relationship. A strong T1 efflux dependent only on the cellular T1 concentration was measured. Although T1 efflux does not explain the effect of K on T1(I) toxicity and uptake, it is responsible for a high turnover of the cellular T1 pool (intracellular half-life = 12-13.5 min). No effect of Na+, Mg2+, or Ca2+ was observed on T1+ uptake, whereas the absence of trace metals (Cu, Co, Mo, Mn, Fe, and Zn) significantly increased T1 uptake and decreased the mitigating effect of K+. The importance of K+ in determining the aquatic toxicity of T1+ underscores the use of ambient K+ concentration in the establishment of T1 water-quality guidelines and the need to consider K in predicting biogeochemical fates of T1 in the aquatic environment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it