The acoustic dead zone: theoretical vs. empirical estimates, and its effect on density measurements of semi-demersal fish
Bibliographic record
Abstract
Abstract Mello, L. G. S., and Rose, G. A. 2009. The acoustic dead zone: theoretical vs. empirical estimates, and its effect on density measurements of semi-demersal fish. – ICES Journal of Marine Science, 66: 1364–1369. The height of the acoustic dead zone, the region near the seabed where fish cannot be resolved acoustically, was calculated both theoretically (DZt) and empirically (DZe). The DZe was based on measurements of depth and trawl geometry from sensors (SCANMAR) mounted on a bottom trawl deployed during a survey off Newfoundland and Labrador in winter 2007. Acoustic data were acquired while trawling, using a 38-kHz echosounder (Simrad EK500) with a hull-mounted transducer. The DZe was calculated as the difference between the trawl-footrope depth and the corresponding acoustically sensed, seabed depth. EK500 and SCANMAR estimates of seabed depth were significantly different. The fish caught were mostly Atlantic cod (Gadus morhua). The estimates of DZe ranged between 2.0 and 3.5 m and were greater than DZt by 0.1–0.9 m in more than half the cases. Three values of acoustically derived cod densities were estimated for each tow, without dead-zone correction and with corrections for DZt and DZe. When compared with DZt corrections, DZe resulted in negative (6–12%) and positive (9–35%) corrections to cod density. A general linear model revealed that the seabed depth gradient, standard deviation of estimated fish density in the dead zone, and wind direction and force explained 85% of the difference between DZt and DZe estimates. These factors affected the detection of the seabed and biased acoustically derived indices of demersal-fish abundance.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".