Comment [on “Deep‐penetration heat flow probes raise questions about interpretations from shorter probes” by Géli et al.]
Bibliographic record
Abstract
Géli et al. [2001] (Eos, 17 July 2001, p. 317) recently presented three examples of particularly deep marine gravity‐corer temperature observations made to 18 m below the sea floor (mbsf). They noted large deviations of shallow temperatures from the trends of the deep thermal gradients and called into question the accuracy realized with short probes commonly used in detailed heat‐flow studies. We show this to be not a general concern, although attention must be given to perturbations from bottom water temperature variations in some deep ocean locations. We also show that the detail with which geographic and depth variability of heat flow must commonly be defined cannot be achieved with corer‐outrigger observations. Successful interpretation of widely spaced observations like those presented by Géli et al. [2001] requires complementary detailed data collected with multi‐penetration probes.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.060 | 0.042 |
| Insufficient payload (model declined to judge) | 0.008 | 0.014 |
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".