Refining palaeoenvironmental analysis using integrated quantitative granulometry and palynology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it