Refining palaeoenvironmental analysis using integrated quantitative granulometry and palynology
Bibliographic record
Abstract
Accurate palaeoenvironmental analysis is at the heart of producing reliable interpretations and depositional models. This study demonstrates a multivariate statistical approach to facies analysis based on relationships between grain size and quantitative palynology. Our methodology has the advantage that it can be used on small amounts of sample, such as core or well cuttings, as the basis for facies analysis. Proof of concept studies involving the collection of grain-size and palynological datasets from well-exposed outcrops of the Middle Jurassic, Lajas Formation of the Neuquén Basin, Argentina, demonstrate that canonical correspondence analysis can be used to consistently recognize facies and aid in the determination of depositional environments. This study demonstrates the link between depositional facies, grain-size distribution, palynomorph hydrodynamics and assemblage taphonomy of palynomorphs. This knowledge can be transferred into a semi-automated statistical facies prediction technique for the subsurface in complex depositional settings, particularly when calibrated against conventional sedimentary facies analysis. Supplementary material: The full set of grain-size data and statistical scores are available at: https://doi.org/10.6084/m9.figshare.c.3745481.v1
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".