An assessment of benthic species richness and macroalgal habitat near Igluligaarjuk (Chesterfield Inlet), Nunavut, using ROV exploration
Bibliographic record
Abstract
There is limited information on the diversity and distribution of nearshore (depths < 20 m) benthic fishes, invertebrates, and their associated habitats near Chesterfield Inlet, which resides in the Southampton Island Area of Interest (SI AOI). Coastal community-led fieldwork was completed between July 25th and 26th, 2023 near the hamlet of Chesterfield Inlet, to address these knowledge gaps and categorize the nearshore benthic ecosystem. This fieldwork included a unique approach to non-invasive ecosystem observation and monitoring, using a remotely operated vehicle (ROV) that was deployed at sites selected by the Aqigiq Hunters and Trappers Organization (AHTO). Using BIIGLE, a web-based application designed for the annotation of images and videos, benthic invertebrates, fish, and macroalgae were identified and labelled to the lowest possible taxonomic level. Analyses also included categorizing the habitat of each site with respect to substratum, percent vegetation cover, and visibility. There were 12 different macroalgal taxa observed, in which the dominant taxa were sieve kelp, Agarum clathratum, sugar kelp (Saccharina latissima), and witch’s hair (Desmarestia aculeata). Two fish species were identified, the Arctic shanny (Stichaeus punctatus) and the banded gunnel (Pholis fasciata), which were observed within the kelp. Numerous invertebrates were observed among 32 total faunal taxa, such as cone worms (Cistenides sp.), stalked jellies (Stauromedusae), and various sea stars (Asteroidea). These data will contribute to better understanding the benthic biodiversity and associated habitats in Hudson Bay, and the method for analyzing underwater footage will provide an option for gathering biodiversity data without disturbing benthic habitats.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".