Bibliographic record
Abstract
There are good prospects of natural gas hydrate resources on the northern slope of the South China Sea. However, whether or not the gas hydrate will play a part in our energy strategy lies in whether or not it can be effectively extracted. Such real production will mostly rely on the full assessment of gas production potential from the marine hydrate deposits. Unfortunately, the relevant research costs are surprisingly high in the field tests alone, while the experimental simulation of hydrate production under high pressures is limited to a small scale presently, and can not fully characterize the gas production potential on a substantial reservoir scale. Thus it is the only way for hydrate production engineers to establish mining plans and calculate gas production potentials through numerical simulation. Because the gas production potential, however, is closely related to the complexity and dissimilarities of the hydrate reservoir characteristics, the production capacities must be evaluated by using different production methods for different types of hydrate reservoirs, which will inevitably raise the research cost. Therefore, based on the response relationship between hydrate production potential and different geological parameters of hydrate reservoirs, this paper discussed the geological parameters, which are most closely related to the gas production potential. Thus, a set of the objectives, contents and methods were presented for the geological evaluation indexes of marine hydrate production potential, providing the theory and methodology for the rapid evaluation of gas production potential in the marine gas hydrate reservoirs.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".