Contrasting Pathways of Assimilation
Bibliographic record
Abstract
Fish are commonly used for monitoring the quality of waters receiving pulp mill effluents (PMEs). Isotopic assays of fish tissues have the potential to provide empirical evidence to link an effluent source to exposure. We show in a 45-d factorial laboratory experiment that different exposure pathways lead to isotopic signatures in fish tissue. Rainbow trout (Oncorhynchus mykiss) were exposed to 10% PME in three ways; direct exposure through addition of PME to aquaria, indirect exposure through invertebrate food consumption (Chironomus tentans cultured in 10% PME), and a combination of both exposure pathways. Of the four stable isotopes measured (delta13C, delta15N, delta34S, delta37Cl), delta13C, delta34S, and delta37Cl showed significant differences in exposed animal tissues. Delta37Cl of fish muscle tissue showed consistent differences across trophic levels and revealed contrasting pathways of PME exposure. Contrasting delta 37Cl values in C. tentans due to the presence or absence of 10% PME did not translate into delta37Cl differences in fish. Rather, delta37Cl ratios of fish muscle tissue were specifically related to 10% PME exposure in the aquaria (waterborne exposure pathway). Feasible distributions of 37Cl source contributions for observed mixture ratios confirmed that PME accounted for observed differences in delta37Cl among treatments. Direct uptake of chloride ions across gill surfaces is the most likely pathway for assimilation of PME into fish tissues. Considering the variability of PMEs and receiving environments, use of a multi-isotope approach is recommended for tracing exposure of fish. Use of 37Cl should also be considered in light of its alternative assimilation pathway.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".