Cataloguing and monitoring changes in Arctic marine biodiversity through SCUBA diving
Bibliographic record
Abstract
Since 2014 divers from Ocean Wise have been SCUBA diving in the Cambridge Bay, Nunavut area collecting data on fishes, invertebrates and marine plants at numerous sites. For each dive a file is created that catalogues the species found and a rough abundance of that species. These files accumulate over time and are searchable by location, year, year and month, month, species and a number of other criteria with custom software created for this purpose. Relationships between species is automatic with the searches. In addition to the species catalogue that began in 2014, data has been scrounged from previous collecting trips by staff and personal dive logs before 2014, allowing for comparison between Pond Inlet, Resolute and Cambridge Bay. We were able to flag a potential decline in one species in 2017 thanks to our previous data. Our goal is to work to cooperatively with others diving in the Arctic to grow this database through photography and dive records. At this point we have 149 dives/records and 279 species recorded. The database is used to support the Nearshore Ecological Surveys and the Arctic Marine Ecological Benchmarking Program reports. In addition to biodiversity data, temperature, salinity, pH and dissolved oxygen are also collected while in the area.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".