Heat Flow in the Western Arctic Ocean (Amerasian Basin)
Bibliographic record
Abstract
Abstract From 1963 to 1973 the U.S. Geological Survey measured heat flow at 356 sites in the Amerasian Basin (Western Arctic Ocean) from a drifting ice island (T‐3). The resulting measurements, which are unevenly distributed on Alpha‐Mendeleev Ridge and in Canada and Nautilus Basins, greatly expand available heat flow data for the Arctic Ocean. Average T‐3 heat flow is ~54.7 ± 11.3 mW/m2, and Nautilus Basin is the only well‐surveyed area (~13% of data) with significantly higher average heat flow (63.8 mW/m2). Heat flow and bathymetry are not correlated at a large scale, and turbiditic surficial sediments (Canada and Nautilus Basins) have higher heat flow than the sediments that blanket the Alpha‐Mendeleev Ridge. Thermal gradients are mostly near‐linear, implying that conductive heat transport dominates and that near‐seafloor sediments are in thermal equilibrium with overlying bottom waters. Combining the heat flow data with modern seismic imagery suggests that some of the observed heat flow variability may be explained by local changes in lithology or the presence of basement faults that channel circulating seawater. A numerical model that incorporates thermal conductivity variations along a profile from Canada Basin (thick sediment on mostly oceanic crust) to Alpha Ridge (thin sediment over thick magmatic units associated with the High Arctic Large Igneous Province) predicts heat flow slightly lower than that observed on Alpha Ridge. This, along with other observations, implies that circulating fluids modulate conductive heat flow and contribute to high variability in the T‐3 data set.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".