Metal (Ag, Cd, Cu, Ni, Tl, and Zn) Binding to Cytosolic Biomolecules in Field-Collected Larvae of the Insect <i>Chaoborus</i>
Bibliographic record
Abstract
We characterized the biomolecules involved in handling cytosolic metals in larvae of the phantom midge (Chaoborus) collected from five mining-impacted lakes by determining the distribution of Ag, Cd, Cu, Ni, Tl, and Zn among pools of various molecular weights (HMW: high molecular weight, >670-40 kDa; MMW: medium molecular weight, 40-<1.3 kDa; LMW: low molecular weight, <1.3 kDa). Appreciable concentrations of nonessential metals were found in the potentially metal-sensitive HMW (Ag and Ni) and LMW (Tl) pools, whereas the MMW pool, which includes metallothioneins (MTs) and metallothionein-like proteins and peptides (MTLPs), appears to be involved in Ag and Cd detoxification. Higher-resolution fractionation of the heat-stable protein (HSP) fraction revealed further differences in the partitioning of nonessential metals (i.e., Ag = Cd ≠ Ni ≠ Tl). These results provide unprecedented details about the metal-handling strategies employed by a metal-tolerant, freshwater animal in a field situation.
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 | 0.000 |
| 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 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".