Passive acoustic monitoring of haddock in the Gulf of Maine: Preliminary results
Bibliographic record
Abstract
A “Passive Acoustic Monitoring” (PAM) survey of haddock sounds was conducted in collaboration with commercial fishers in the inshore regions of the Gulf of Maine (GOM) during 2003-2004 and 2006-2007 using bottom mounted “Autonomous Underwater Listening Stations” (AULS). Haddock sounds were observed in 34 of 59 deployments, however call rates were highly variable both spatially and temporally. Haddock sounds averaged 0.7 call/h and 40.5 knocks/h. A strong nocturnal spawning pattern was observed. A significant correlation between haddock call rate and the “Catch Per Unit Effort” (CPUE) of ripe-and-running female haddock demonstrates that PAM is a potentially powerful tool to supplement stock assessment surveys. Haddock sounds indicative of spawning behavior were observed well into July suggesting that spawning of inshore populations of haddock in the GOM extends over a longer season than generally thought. Further the intro- and inter-annual variability in diel periodicity of both haddock sound production and CPUE of haddock in spawning condition suggest spawning behavior is highly plastic in the species and caution is advised when attempting to extrapolate observation from one region to another. This study demonstrates that PAM is an important new tool that can provide supplemental data to traditional fisheries data for haddock and other soniferous fishes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".