Biodiversity and habitat assessments in the coastal benthic ecosystems near Rankin Inlet 2023-2024
Bibliographic record
Abstract
Benthic habitats and species compositions across western Hudson Bay remain a knowledge gap, which is critical given the importance of this marine ecosystem to local communities. To address this, fieldwork took place during July 2023 and 2024 within Rankin Inlet at localized sites selected by the Kangiqłiniq Hunters and Trappers Organization (KHTO) to categorize the benthic habitat. Benthic grabs, dredges, and a non-invasive approach utilizing a remotely operated vehicle (ROV) were used to observe the benthos and obtain physical samples. Video footage was examined using BIIGLE, a web-based application designed for video and photo annotation, to identify taxa alongside varying habitats. In total, 47 faunal taxa were identified, alongside 11 macroalgal taxa. Sizes were measured across 20 individuals from the 2024 footage using a novel approach developed here to assess size dimensions within the image editing tool, GIMP. Among bottom grabs, 49 different taxa were identified with little overlap from the ROV footage. Arthropods, annelids, and molluscs were the only taxa caught, with varying relative abundances across sites and catch methods. Melvin Bay, had the highest relative abundance of caught invertebrate taxa. These data will contribute to a greater understanding of the benthic diversity and habitats in the nearshore waters of Rankin Inlet and surrounding islands. Moreover, the methods for analyzing underwater footage will provide options for future studies to gather baseline data using non-invasive measures.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".