Influence of Environmental Variables on the Diel Movements of the Greenland Shark (<i>Somniosus microcephalus</i>) in the St. Lawrence Estuary
Bibliographic record
Abstract
The geographic distribution of the Greenland Shark (Somniosus microcephalus) extends from the Arctic Ocean to the North Atlantic Ocean. However, little is known about the habitat of this species, as it is generally found at great depths or in the High Arctic. In the St. Lawrence Estuary, Greenland Sharks undertake diel vertical movements into shallow water (≤ 30 m), but the reasons for these movements are unknown. To test the hypothesis that environmental variables drive the movements of this shark in the St. Lawrence Estuary, eight Greenland Sharks were tagged with acoustic telemetry transmitters during the summer of 2005. Three environmental factors, temperature, light, and tides, were associated with their movements. Movement patterns indicate a preference for deep, cold water during daylight hours and shallow, warmer water during the night. Ascending into shallow water mostly coincided with darkness and high tide. This improved understanding of the spatio-temporal distributionof the Greenland Shark will allow for assessment of the risk to these sharks from commercial fisheries, as occurs in the Greenland Halibut (Reinhardtius hippoglossoides) longline fishery. In addition, temperature-driven behavioural patterns may change as the thermal structure of the water column shifts due to global warming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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 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".