Surface heat flux histories from inversion of geothermal data: Energy balance at the Earth's surface
Bibliographic record
Abstract
Past changes in the Earth's surface energy balance propagate into the subsurface and appear as perturbations of the subsurface thermal regime. This paper presents a singular value decomposition inversion method used to reconstruct surface heat flux histories (SHFH) from the heat flux anomalies detected in the shallow subsurface. Synthetic tests were used to assess the robustness of the inversion procedure. It was found that data noise can have a significant effect on the stability of the SHFH inferred from inversion. This translates in SHFHs having lower temporal resolution than ground surface temperature histories (GSTHs) obtained from the same data, but the long‐term trends are robust. Results are encouraging for temperature data noise levels typically encountered in field measurements. Synthetic data tests yield results in agreement with analytical expressions derived from GSTHs for the same parameterization. Temperature‐depth profiles from Canada's geothermal database were used to illustrate the inversion procedure. Individual temperature profile inversions are shown as examples. All 112 temperature logs in the database were used to obtain a mean heat flux history for the region. Results indicate that the ground heat flux has increased an average of 24 mW m −2 over the last 200 years in Canada. Application of this method to the existing global geothermal data base should allow for a quantification of the global energy balance at the Earth's surface for the past few centuries and may be useful for land surface 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".