Data from: An integrated approach to understanding noise stress in two auditorily diverse species of freshwater fish
Bibliographic record
Abstract
Anthropogenic noise can have negative consequences on a fish’s ability to survive and reproduce and has been increasing over the past decades. To determine the integrated effects of anthropogenic noise on two auditorily diverse freshwater fish species, yellow perch (Perca flavescens) and spottail shiner (Notropis hudsonius), the current study assessed behavioural, physiological, and transcriptional metrics. Fish were exposed to anthropogenic noise or control sounds in a semi-captive field setting, and behavioural changes, blood cortisol levels, and relative transcription were analyzed for effects of anthropogenic noise. Results showed little effect of noise on yellow perch; however, spottail shiners were affected by noise at multiple levels of organization. Additionally, blood cortisol levels showed indications of handling stress in both species, overriding any potential effects of noise. For the first time, the current study differentiates stress effects from handling and those induced by environmental noise and shows that ecological impacts of anthropogenic noise can be both species- and metric-specific, indicating a need for a more systematic approach when analyzing potential noise impacts.
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.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".