Bibliographic record
Abstract
Several genetic mechanisms of less mature oils have been proposed by previous researchers, and solvable organic matter generating hydrocarbons in early evolution stage has been suggested as one of the most important mechanisms for considerable immature reserves in rifted-basins such as the Jinhu depression, North Jiangsu-South Yellow Sea basin and Bamianhe oilfield, Bohai Bay basin of East China. However, our recent investigations show that some of the genetic models of less mature oils cannot be used to guide oil exploration. Oil-rock corrections have indicated that the so-called immature oils of the two basins are mainly derived from intervals located in normal oil-windows. There is hardly correlation between the alga-rich Es4 shale developed in brackish environments of the south slope of the Nuzhuang sag, Dongying depression and Bohai Bay basin and the crude oils in Bamianhe Oilfield in terms of the distribution patterns of steroids especially dinostanes. The results suggest that solvable organic matter in shale with buried depths less than 2700 m of the area, which was considered to be the main source rock of Bamianhe Oilfield in previous studies, is unlikely to be the essential source of the oils. Immature oils mostly coexist with adjacent favorable oil-kitchens in East China and the fact that the so-called immature oils of the typical immature oilfield i. e. the North Jiangsu-South Yellow Sea basin have features of mature oils infer that oil-mixing is a common phenomenon in some Tertiary rifted-basins of East China. Some methods of resource assessment may not be suitable for less mature oils if other genetic mechanisms rather than the traditional kerogen degradation account for immature oils. A new assessment method showes that the amount of immature resources in the area studied is not as large as previously calculated. Reinvestigation is necessary in basins bearing immature oils, adopting new analysis techniques such as biomarker quantification. It will be useful in determining the real maturity and the proportion of mixed oils with different sources.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".