o f Marine Mammals Using Passive Acoustics was held in
Bibliographic record
Abstract
The main objective of this workshop was to provide a forum at which interested parties could compare their detection and localization algorithms with those of others, identify the advantages and limitations of the various techniques, as well as their relative accuracy and efficiency. For this purpose, a common dataset was made available to the participants by DRDC Atlantic and Dalhousie University. After initial distribution, the Cornell Laboratory of Ornithology offered an additional dataset to expand the base for detection algorithms. These datasets are described in detail in these proceedings. The workshop was divided into four sessions: background presentations, detection and classification, localization, and discussion periods. The background presentations provided examples of passive detection and localization for the purpose of species conservation or mitigation. The participants presented their algorithms during the detection and localization sessions. During the discussion periods, participants compared results obtained from the workshop datasets, different detection and localization technologies, and the possibilities for automation and future collaboration. This short paper recaps the techniques that were presented, as an introduction to the papers that were submitted in these proceedings. It also summarizes the discussions, and some of the highlights from the workshop.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".