Development of a Library of Responses for an Early-Warning Biomonitoring System to Detect and Identify Various Aquatic Contaminants
Bibliographic record
Abstract
Biomonitors can be implemented in aquatic ecosystems to continuously assess water quality, but existing monitors are still reliant on a single species and unable to identify any stressor. A library of responses could potentially address these drawbacks by stereotyping the responses of several aquatic species to different contaminants. A model for the library was developed by conducting a bioassay on Pseudokirchneriella subcapitata and collecting the response data of Daphnia magna, Hyalella azteca and Lumbriculus variegatus from published ecotoxicological studies. Multivariate statistical tools were then employed to process the response data set and evaluate the ability of the model to distinguish contaminations by atrazine and tributyltin. Based on preliminary tests, the library was able to detect and identify each contaminant within 4 hours with an accuracy of 97%. These findings supported the integration of a library of responses in a biomonitoring system to provide a more comprehensive water quality assessment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".