Baffin Bay/Nares Strait surface (seafloor) sediment mineralogy: further investigations and methods to elucidate spatial variations in provenance
Bibliographic record
Abstract
The goal of the paper is to ascertain whether there are significant regional variations in sediment mineral composition that might be used to elucidate ice sheet histories. The weight percentages of nonclay and clay minerals were determined by quantitative X-ray diffraction. Cluster analysis, an unsupervised learning approach, is used to group sediment mineralogy of 263 seafloor/core top samples between ∼80°N and 62°N. The optimum number of clusters, based on 30 indexes, was three for the weight percentage data but varied with data transformations. Maps of the distribution of the three mineral clusters or facies indicate a significant difference in weight percentages between samples from the West Greenland and Baffin Island shelves. However, several indexes support a larger number of clusters and similar analyses of the spatial distribution and defining minerals of nine mineral facies indicated a strong association with the original three clusters and with broad geographic designations (i.e., West Greenland shelf, Baffin Island fiords, etc). Classification Decision Tree analysis indicates that this difference is primarily controlled by the percentages of plagioclase feldspars versus alkali feldspars.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".