Characteristics of Mudstone in Complex Fluvial Sedimentary System in Bohai L Oilfield
Bibliographic record
Abstract
Bohai L oilfield develops a complex fluvial sedimentary system, which includes many types of fluvial sedimentary facies. Based on the coring well of oil field, the distribution characteristics of mudstone are analyzed, it is shown that the mudstone has similar internal structure and can be classified into four types according to its color: gray to grayish, variegated, brown to gray-brown and khaki, the assemblage has a large gray to grayish mudstone section, large gray to grayish mudstone intercalated thin layer sandstone, gray to grayish mudstone associated with variegated (brown) mudstone, grayish mudstone associated with lacustrine sand grain bedding sandstone, concomitant generation of large staggered bedding sandstone and grayish mudstone, mixed (gray-brown) mudstone associated with large staggered bedding sandstone, interaction between different colors of mudstone and sandstone and large interlaced sandstone intercalated with thin layer mudstone. The mudstone color is mainly gray and grayish, and very few oxidized mudstone is developed alone, which indicates that fluvial mudstone may be formed in the reductive environment in humid climate, and the fluvial mudstone in this area may be formed in the oxidizing environment, which is different from the general understanding of fluvial facies, the oxidation color is the result of the later transformation.
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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 | 0.003 | 0.001 |
| 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) | 0.001 | 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".