Arctic marine ecology benchmarking program: Monitoring biodiversity using scuba
Bibliographic record
Abstract
Having reliable baseline data and carrying out ongoing monitoring are important to fully understanding the changes underway in Canada’s Arctic. This knowledge will enable effective management strategies and conservation plans to be developed. However, very few surveys of nearshore marine flora and fauna in the Canadian Arctic have been conducted. This project gathered biodiversity data at key sites near Cambridge Bay, Nunavut. Long-term marine nearshore ecosystem monitoring was also started. Since 2014, the Ocean Wise Conservation Association and Polar Knowledge Canada have surveyed 26 nearshore sites using scuba diving in the region around Cambridge Bay. Data on habitat type and species diversity were collected. The 2017 Arctic Marine Ecology Benchmarking Program marks the next stage of the research. This involved shifting from exploration and cataloguing to systematic documentation and ecological benchmarking. The 2017 benchmarking program scientific dive team quantified the biodiversity and abundance of marine algae, invertebrates, and fish species at six selected sites in the Cambridge Bay area. This effort serves as a pilot study to assess how the survey is designed and to make recommendations for future research and monitoring efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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; both teacher heads agree on what is shown here.
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".