Temperature measurements and thermal gradient estimates on the slope and shelf-edge region of the Beaufort Sea, Canada
Bibliographic record
Abstract
In situ temperature measurements were conducted at 63 gravity-core stations during the 2013 expedition with the CCGS Sir Wilfrid Laurier in the Canadian Beaufort Sea. Outriggers attached to the outside of the gravity core-barrel were used to mount portable miniature temperature loggers (MTL) for down-core in situ temperature measurements. Several sub-regions were investigated during the expedition including two shelf-slope crossings, three mud volcano-type expulsion features, as well as two canyon sites. The last site visited was at the Gary Knolls, just east of the Mackenzie Trough at water depths of less than 100 m. Overall, temperature data obtained from the MTLs were of high quality at most stations and the data acquisition technique was proven to be robust and easy to adapt in the Arctic. However, depth determination for each logger position remains the largest challenge as no additional pressure sensor was used with the MTLs. Instead, depths were estimated based on the apparent core penetration and the geometry of the outriggers. The most significant result from this work is the discovery of the very large apparent geothermal gradients associated with the two expulsion features (EF) Coke Cap and the mud volcano at 420 m water depth. Temperatures measured within the top 2.5 meter below seafloor suggest geothermal gradients of up to 2.94ºC/m (Station 96, 420m EF) and 1.37 ºC/m (Station 58, Coke Cap EF). Away from the centre of the EFs, thermal gradients decrease to values of 0.5ºC/m for Station 99 at the 420 m EF, and 0.92ºC/m at Station 21 at the Coke Cap EF. Temperature data across the slope-shelf transect and the two transects across the canyon heads did not reveal considerable geothermal gradients, but show a water-depth dependent trend in temperature. From deep to shallow water, temperature appear to decrease until the most negative temperature values are found on the shelf itself at water depths of ~100 m (-1.2 to -1.4ºC). Overall, data from the top 1.0 to 1.5 meter below seafloor are likely affected by seasonal variations in the water column temperature and may not be used to define geothermal gradients. With an optimal full penetration of the core barrel, the deepest temperature data are from ~2.3 mbsf, which limits the accuracy of the estimated geothermal gradients as only few data points (2 - 4) can be used in the calculations.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".