Fish species identification using image analysis of echo-sounder images
Bibliographic record
Abstract
Acoustic surveys for marine fish in coastal waters typically involve identification of species groups. Incorrect classification can limit the usefulness of both distribution and biomass estimates. Fishing catch data can assist in identification, but are rarely spatially comparable to acoustic data and are usually biased by gear type. This thesis describes a technique and a software toolkit, FASIT (Fisheries Assessment and Species Identification Toolkit), developed by the author to enable automated identification of Atlantic cod (Gadus morhus), capelin (Mallotus villosus), and redfish (Sebastes spp.) based on high resolution acoustic imaging offish aggregations. The approach has been to assess and analyze various amplitude, shape and location features of the acoustic returns from shoals and individual fish, then to use these features to develop algorithms which discriminate among species. Fourteen classifiers based on Three-Nearest Neighbour classification and Mahalanobis distance classification have been implemented and tested. The best classifier had an average correct classification rate of 96.8%. The data used for this thesis are fisheries data from a number of Newfoundland bays and the Grand Bank region collected using a 38 KHz digital echo-sounder.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".