Bibliographic record
Abstract
Peak heat flow occurs at mid-ocean ridges and decreases with the age of the oceanic lithosphere. Several plate models, including the Parsons and Sclater (PSM) model, Global Depth and Heat (GDH1) model and Constant Heat flow Applied on the Bottom Lithospheric Isotherm (CHABLIS) model, have been used to predict heat flow in the ocean lithosphere. The discrepancy between the predicted and measured heat flow in the younger lithosphere (i.e. younger than 55 Myr) influenced by local hydrothermal circulation has been used to estimate hydrothermal heat flux and investigate hydrothermal processes. We can modify the cooling models by substituting the ordinary mass density of lithosphere by fractal density with singularity. This new model provides a modified solution to fit the observed heat flow data used in other models in the literature throughout the age range. This model significantly improves the results for prediction of heat flow that were obtained using the PSM, GDH1 and CHABLIS models. Furthermore, the heat flow model does not exhibit special characteristics around any particular age of lithosphere. This raises a fundamental question about the existence of a "sealing" age and accordingly the hydrothermal flux estimation based on the cooling models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".