Thermal History Reconstruction of the Sedimentary Basin by Inverse Modeling on the Example of CDP 1632 of the Okhotsk Sea
Bibliographic record
Abstract
Summary Basin and petroleum systems modeling in the poorly studied oil and gas provinces like the Okhotsk Sea region leads to significant uncertainties in the simulation results, which is reflected in the risk assessment. It requires using modern methods that allow working with a minimum input dataset while capturing all key geological processes. The potential of one of these methods - automated thermotectonostratigraphic approach or inverse modeling - is demonstrated through a basin thermal history reconstruction case study on the example of CDP 1632. Being based on rare published data, two different scenarios that are based on rare limited published data are considered for the tectonic evolution of the basin. These scenarios demonstrate different thermal histories and organic matter evolution, despite the successful calibration using a limited dataset on temperature and vitrinite reflectance. The additional use of gravimetric data allows us to improve the model. It is the first application of this approach in the region of the Far East, so uncertainty in modeling results, as well as the pros and cons of the approach, are discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".