Review of aquatic in situ approaches for stressor and effect diagnosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Field-based (in situ) approaches are used increasingly for measuring biological effects and for stressor diagnoses in aquatic systems because these assessment tools provide realistic exposure environments that are rarely replicated in laboratory toxicity tests. Providing realistic exposure scenarios is important because environmental conditions can alter toxicity through complex exposure dynamics (e.g., multiple stressor interactions). In this critical review, we explore the information provided by aquatic in situ exposure and monitoring methods when compared with more traditional approaches and discuss the associated strengths and limitations of these techniques. In situ approaches can, under some circumstances, provide more valuable information to a decision maker than information from surveys of resident biota, laboratory toxicity tests, or chemical analyses alone. A decision tree is provided to assist decision makers in determining when in situ approaches can add value.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it