Multivariate modeling of glacimarine lithostratigraphy combining scanning XRF, multisensory core properties, and CT imagery: IODP Site U1419
Bibliographic record
Abstract
Marine sediments preserve archives of glacier behavior from many proxies, with lithofacies analysis providing direct evidence of glacial extent and dynamics. Many of these lithofacies have corresponding physical and geochemical properties that may be identified through quantitative, nondestructive logging properties. This study applies supervised and unsupervised classification to downcore logging data to attempt to model temperate glacimarine facies, which are independently identified via visual lithofacies analysis based on core photographs, digital X-radiography, and computed tomography scans. We test the limits of these methods by modeling both broad glacial and interglacial and small-scale variations in Late Pleistocene (<60,000 yr) glacier extent leading into the Holocene deglaciation for a temperate ice stream at Integrated Ocean Drilling Program Site U1419 in the Gulf of Alaska. Multi-meter–scale mud and diamict lithofacies interpreted as non-glacial versus glacial conditions can be modeled with both methods using downcore physical property logging data (b* color reflectance, magnetic susceptibility, and natural gamma-ray activity) augmented with scanning X-ray fluorescence (XRF) elemental abundance (Ca, Zr, Si, K, Rb, and Al). Physical properties are most useful for delineating decimeter-meter–scale variations in composition and clay content, whereas scanning XRF elements best capture differences in sand versus clay content and composition at decimeter-centimeter scales. Neither classification technique can model the observed small-scale variations in diamict facies using elemental abundance from higher-resolution scanning XRF or from physical properties. Comparison of unsupervised cluster model results with observed lithofacies allows for identification of three different glacial conditions at Site U1419—ice-proximal, fluctuating, and retreating. For small-scale variations in glacial extent, cluster model results are best used as complementary data to image-based lithofacies identification rather than as a replacement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".