Preliminary Results from the 2024 August Ecosystem Survey in the Estuary and Northern Gulf of St. Lawrence
Bibliographic record
Abstract
Fisheries and Oceans Canada conducts an annual multidisciplinary survey in the Estuary and the northern Gulf of St. Lawrence. The objectives of this survey are varied: estimate the abundance of groundfish and invertebrates; assess physical, chemical and biological (phytoplankton and zooplankton) oceanographic conditions; assess the biodiversity of species found in the demersal zone; monitor the pelagic ecosystem; and collect samples for various research projects. In 2024, the survey was conducted between August 2 and September 4 on board the Canadian Coast Guard Ship (CCGS) John Cabot. During this survey, 163 trawl tows were completed. In addition, 79 vertical profiles of the water column were carried out to characterize oceanographic conditions and 41 zooplankton samples were also collected. This report presents the results of catches of the successful tows. In total, 79 fish taxa and 212 invertebrate taxa were identified during the survey. Historical perspectives (catch rates, spatial distribution and length frequency) are presented for 26 taxa. These commercial fishery-independent data will be used in several stock assessments including Atlantic Cod (Gadus morhua), Redfish (Sebastes spp.), Greenland Halibut (Reinhardtius hippoglossoides), Atlantic Halibut (Hippoglossus hippoglossus), Witch Flounder (Glyptocephalus cynoglossus), and Northern Shrimp (Pandalus borealis). The preliminary analysis of water temperature measurements in 2024 shows conditions that have slightly cooled at depths of 150 m and more for a second consecutive year, since the centennial records reached in 2022. The summer temperature of the cold intermediate layer was similar to that of 2023; it would be the 4th warmest that has been measured with modern instruments since 1985. The surface water temperature was above normal during the periods of July-August and May-August, close to record values.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".