Application of the biotic ligand model to explain potassium interaction with thallium uptake and toxicity to plankton
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".