Modeling on Gas Hydrate Formation Conditions in the Qinghai‐Tibet Plateau Permafrost
Bibliographic record
Abstract
Abstract Based on the field‐investigated gas geochemistry, the modeling of gas hydrate formation conditions is conducted in the Qinghai‐Tibet plateau permafrost (QTPP) in combination with predecessors' data such as the permafrost ground temperature ( T 0 ), the thermal gradient within the frozen layer ( G 1 ) and the thermal gradient below the frozen layer ( G 2 ). The modeled results show that the permafrost characteristics generally meet the requirements for gas hydrate formation conditions in the study area. Gas composition, temperaturerelated permafrost parameters (e.g. T 0 , G 1 , G 2 ) are the most important factors affecting gas hydrate formation conditions in the study area, whose spatial variations may cause the heterogeneity of gas hydrate occurrences. The most probable gas composition to form gas hydrate is the hybrid of methane and weight hydrocarbon gases (ethane and propane). In the predicted gas hydrate locations, the minimal upper depth of gas hydrate occurrence is less than one hundred meters and the maximum lower depth can reach one thousand meters with the thickness up to several hundred meters. Compared with Canadian Mallik gas hydrate field, the QTPP is favorable for gas hydrate formation in aspects of G 1 , G 2 and gas composition, except for relatively thin permafrost, still suggesting great gas hydrate potentials.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".