Assessing behavioural and physiological responses of three aquatic invertebrates to tributyltin and atrazine in a multi-species, early warning biomonitoring technology
Bibliographic record
Abstract
Recent global events and anthropogenic changes to the natural environment have raised concerns about the quality of drinking water consumed by the public throughout the world. Traditional chemical testing is slow and is not performed for all possible contaminants, necessitating the development of innovative new technology to detect and mitigate threats to human health. The development of a multi-species, early-warning biomonitoring technology, based on behavioural and physiological changes in aquatic organisms, greatly furthers this goal. In this study, changes in movement behaviour and respiration rates were examined in three aquatic species, Daphnia magna, Hyalella azteca and Lumbriculus variegates, exposed to varying concentrations of TBT and atrazine, using digital video analysis and direct oxygen measurement. Different parameters of movement were examined and evaluated for inclusion in a multi-species, early-warning biomonitoring technology and the utility of incorporating these parameters into a model to determine classes and concentrations of various contaminants is discussed. An evaluation of whether or not direct measurement of oxygen consumption rates is feasible and useful for inclusion in a multi-species, early-warning biomonitoring technology is also discussed.
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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 | 0.000 | 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".