Deep‐ocean temperature variations and implications for errors in seafloor heat flow determinations
Bibliographic record
Abstract
The accuracy with which seafloor heat flow is determined depends on the temporal stability of bottom water temperature. Indirect tests for stability are provided most commonly by observing the uniformity of heat flow with depth. This criterion is met to a high degree of certainty at two sites in the eastern North Pacific Ocean, where colocated high‐quality probe and borehole heat flow data can be compared. A more direct test for stability is provided by long‐term observations of bottom water temperature. Previously published records and new data show temperature variations of only a few hundredths of a degree at sites in the central and eastern North Atlantic and the eastern North Pacific. Resultant gradient perturbations are geothermally insignificant (<5 mK m−1) at depths greater than 1–2 m below the seafloor, consistent with the uniformity of heat flow with depth observed in these areas. Geothermally problematic bottom water temperature variations are observed or inferred in the western North and South Atlantic and western South Pacific. Variations range up to ±0.15 K and are capable of producing gradient perturbations of up to 5 mK m−1 at depths as great as 5 m below the seafloor. While these data are instructive, their distribution is not adequate to provide general guidelines for estimating geothermal gradient perturbations. Data from shallower sites are needed in all oceans to define depth limits of acceptable bottom water temperature variability, and from other deep‐ocean locations where near‐source bottom water transients or vigorous deep‐water circulation dynamics are likely to be present.
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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 | 0.006 | 0.037 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".