Bibliographic record
Abstract
Acoustic deterrent technologies can be used in aquatic settings in lieu of physical barriers to keep fish away from potentially harmful industrial operations. The goal of this study was to determine the efficacy of portable, temporary acoustic deterrents as a means of excluding fish from the neighborhood of sub-bottom detonation activities associated with seismic exploration under ice in arctic lakes. In October 2003 trials were conducted in Dolomite Lake near Inuvik, Northwest Territories, Canada on indigenous fish species. Groups of fish were equipped with orally inserted ultrasonic tags, placed in a large experimental net pen and monitored using an acoustic tracking system that produced a detailed three-dimensional swimming pattern for each subject, thereby revealing any behavioral responses. A flex-tensional broadband sound projector driven by digitally synthesized signals was tested as deterrent, and real-time monitoring at two hydrophone sites was used to estimate the local level of insonification throughout the pen volume. Although the study did not identify an overall effective deterrent, sufficient indications of response were observed to support the future testing of a louder projector capable of emitting a tonally modulated sound pattern at frequencies from about 100 Hz to a few kHz. [Work supported by ESRF (esrfunds.org).]
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".