Acoustic sea bed classification of Pacific Sand Lance habitat.
Bibliographic record
Abstract
This paper describes the results of preliminary acoustic sea bed classification surveys in three areas in the southern Gulf Islands of British Columbia to develop methods for mapping habitat of Pacific Sand Lance (PSL). Little is known about this important species, and much less is known about their use of subtidal burying habitat for overwintering. Grid surveys were run using a dual-frequency (24 and 200 kHz) single beam echosounder, and the data were classified using conventional statistical segmentation procedures using QTC IMPACT. This type of seabed classification separates the seabed into self-consistent regions called seabed classes based on acoustic diversity—primarily on the basis of echo shape. Sediment grab samples and video were also obtained to provide ground-truth and labels for the acoustic seabed classes. The acoustic seabed classification surveys were successful in identifying subtidal sands with low fractions of fines (silts) and sand wave fields among a variety of seabed types identified. Grab samples captured PSL buried in the medium to coarse subtidal sands, mostly in sand wave fields, and mostly in winter. Future work will be to observe diel migrations and develop methods to estimate biomass of buried PSL based on seabed 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".