Determining deep-sea coral distributions in the northern Gulf of St. Lawrence using bycatch records and local ecological knowledge (LEK)
Bibliographic record
Abstract
Deep-sea corals have recently received attention due to an increased awareness of their diversity and vulnerability to commercial fisheries. Over 50 species of coral have been identified in Atlantic Canada and the distribution of these species is now fairly well known. However, the deep-sea corals in the Northern Gulf of St. Lawrence have not been previously studied. This study used DFO groundfish survey trawl and fisheries observer records of coral bycatch along with the local ecological knowledge (LEK) of fish harvesters to identify 11 species/groups of deep-sea coral that occur in the Northern Gulf of St. Lawrence (4RSPn) and to map the distribution of seven of these species/groups. Nephtheid soft corals and sea pens (Pennatulacea) are the most common groups occurring in the Northern Gulf. Fish harvester observations on deep-sea coral distributions and coral bycatch in Northern Gulf fisheries are reported along with their opinions on the impacts of different gear types and on protecting corals in the Northern Gulf. Fish harvesters reported that coral bycatch was observed when fishing for eight different target species while using six different gear types and most reported observing a relationship between sea pens and commercial fish species in the Northern Gulf including Atlantic cod (Gadus morhua), Atlantic halibut (Hippoglossus hippoglossus), Greenland halibut/turbot (Reinhardtius hippoglossoides) and Northern shrimp (Pandalus borealis). Fish harvesters’ LEK identified a greater diversity corals than the other two sources of data used, which is likely due to fishing in a wider range of habitats than survey trawls and the longer time periods of observation accessed through fish harvesters’ LEK. There were advantages to using multiple sources of data given the current gaps in our knowledge of deep-sea corals in the Northern Gulf. Each source of data had its own limitations, which are discussed in this thesis, but when used together it was possible to determine deep-sea distribution patterns in the Northern Gulf and to gain insight into the occurrence of deep-sea coral bycatch in Northern Gulf fisheries.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".