Developing an Underwater Video Annotation Methodology for Coastal Communities in Ocean Networks Canada's Oceans 3.0
Bibliographic record
Abstract
High-resolution underwater cabled cameras provide a significant opportunity for species richness and diversity studies. They offer a less invasive methodology and provide substantial data for statistical analysis. Many of Ocean Networks Canada's coastal community observatories are equipped with underwater video cameras. These observatories are designed and customized in consultation with local leaders in whose territories they operate, providing a localized system for year-round continuous, real-time ocean monitoring. This paper proposes an underwater video annotation methodology for Ocean Networks Canada's coastal community observatories. The proposed approach establishes a standardized method for marine species identification and data ingestion. Through open data, technology, and the adoption of a standardized database, individuals can actively contribute to the analysis of video data from these observatories, facilitating the quantification of species numbers and their taxonomy. This research can also be applied in educational initiatives, offering an immersive avenue for the public to explore and understand their surrounding ecosystems while encouraging participation in citizen science.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".