Diel changes in acoustic and catch estimates of krill biomass
Bibliographic record
Abstract
Abstract Simard, Y., and Sourisseau, M. 2009. Diel changes in acoustic and catch estimates of krill biomass. – ICES Journal of Marine Science, 66: 1318–1325. Krill-biomass estimates can be compromised by diel variabilities in acoustic backscatter and the catch efficiencies of various nets. This paper describes an effort to quantify these variabilities at fine temporal and spatial scales during a three-day experiment at a fixed location, using high-resolution, stratified Bioness samples and echo-integration, and assuming a fixed distribution of krill orientations. Night-time catches in the krill scattering layer (SL) were 15 times the acoustic estimates. The situation was reversed during daytime, when the acoustic estimates in the SL were 5 times larger than the catches. This collectively resulted in a ±10‐dB gradual diel cycle in the difference of vertically integrated biomass from both sampling methods. Use of a strobe light on the Bioness reduced avoidance of the net by krill and significantly increased (×10) daytime catches in the SL, but had no significant effect on night-time catches. The difference in volume-backscattering strength at 120 and 38 kHz (ΔSv120–38) in the densest parts of the SL agreed with predictions using a target-strength (TS) model and an assumed normal distribution of tilt (mean θ = 11°; s.d. = 4°). The ΔSv120–38 was smaller for lower densities and during night-time. It appears that the θ and, therefore, TS distributions of krill significantly change during their diel vertical migrations. At twilight and at night, when they are feeding and swimming vertically, they exhibit lower mean TS and ΔSv120–38 and react less to strong strobe-light pulses, in contrast to daytime. Diel patterns in TS and net avoidance should be taken into account in krill-biomass assessments that use round the clock acoustic-survey data and multi-frequency TS models for target classification.
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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 | 0.000 | 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) | 0.001 | 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".