Inverse-forward method for heat flow estimation: case study for the Arctic region
Bibliographic record
Abstract
The heat flow data are important in many aspects including interpretation of various geophysical observations, solutions of important engineering problems, modelling of the ice dynamics, and related environmental assessment. However, the distribution of the direct measurements is quite heterogeneous over the Earth. Different methods have been developed during past decades to create continuous maps of the geothermal heat flow (GHF). Most of them are based on the principle of similarity of GHF values for the lithosphere with comparable age and tectonic history or inversion of magnetic field data. Probabilistic approach was also used to realize this principle. In this paper, we present a new method for extrapolating the GHF data, based on the inversion of a geophysical data set using optimization problem solution. We use the results of inversion of seismic and magnetic field data into temperature and data from direct heat flow measurements. We use the Arctic as the test area because it includes the lithosphere of different ages, types, and tectonic settings. In result, the knowledge of GHF is important here for various environmental problems. The resulting GHF map obtained well fits to the observed data and clearly reflects the lithospheric domains with different tectonic history and age. The new GHF map constructed in this paper reveals some significant features that were not identified earlier. In particular, these are the increased GHF zones in the Bering Strait, the Chukchi Sea and the residual GHF anomaly in the area of the Mid-Labrador Ridge. The latter was active during the Paleogene.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".