Bibliographic record
Abstract
The industrial revolution has driven the need for ecotoxicology and shaped its evolution.Indeed, the increased use and transformation of (non)renewable resources for over a century to benefit mankind have had a downside and created a plethora of contaminants harmful to the receiving environments.With time, we have gone from an age of darkness in the 1950s (i.e., diagnostic ignorance in terms of recognizing and dealing with contamination) to one of enlightenment as the 21 st century unfolds (i.e., use of tools and strategies to identify and correct environmental pollutions).Effects measurements, reflected by toxicity testing conducted at different levels of biological organization, have proven especially useful to achieve proper hazard/risk assessments of contaminants.Knowing why toxicity testing has been conducted over the past decades to protect and conserve freshwater environments is also essential to grasp the importance and breadth of this field.For this purpose, we have recently reviewed a substantial number of articles describing numerous bioanalytical endeavours undertaken to comprehend toxic effects associated with the discharge of xenobiotics to aquatic environments.Scrutiny of publications identified in our literature search has enabled us to uncover the various ways in which laboratory toxicity tests have been applied, many of which are small-scale in nature.In essence, freshwater toxicity testing has significantly focussed on liquid (complex environmental samples, chemical and biological contaminants) and solid media (sediments) assessment.For both media, miscellaneous studies/initiatives linked to toxicity testing applications have again promoted the development, validation, refinement and use of toxicity testing procedures.Bioassays are clearly an essential component of environmental management programs and several small-scale tests (microbiotests) can be employed to generate cost-effective toxicity data that assist decision-making.
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".