Development of a biomarker-based index for assessing the ecotoxic potential of aquatic sites
Bibliographic record
Abstract
The use of biochemical or physiological measurements as indicators of ecotoxicity is under constant development and has the advantage of delineating effects before the appearance of disease. However, these biomarkers are often part of a battery of tests, and it is difficult to integrate them together to gain an overall view of an organism's health. The aim of this study was to develop an index that could integrate the data derived from a battery of biomarkers for application to both spatial and temporal studies. Mya arenaria clams were collected at different sites along the Saguenay Fjord (Quebec, Canada). Six biomarkers were measured: metallothioneins, DNA strand breakage, lipid peroxidation, vitellin-like proteins, phagocytosis, and non-specific esterase activity in haemocytes. A biomarker index was obtained by summing the biomarker values expressed in term of classes. Classes were determined by a distribution-free approach derived from the theory of rough sets. The results of the spatial study show that the index values discriminated well between contaminated and uncontaminated sites. The highly polluted sites had the highest index values (18 compared with a reference value of 14). In the temporal study, the index was also able to highlight possible contamination-induced alterations, even though the interpretation of temporal variation is complicated by natural variations occurring throughout the year. A control chart approach is proposed for determining contaminated sites in both spatial and temporal surveys.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".