Greater than the sum of its parts: optical remote sensing and sediment core data provide a holistic perspective on glacial processes
Bibliographic record
Abstract
Abstract In this letter we make the case that closer integration of sediment core and passive optical remote sensing data would provide new insights into past and contemporary glacio-sedimentary processes. Sediment cores are frequently used to study past glacial processes and environments as they contain a lengthy geochemical and sedimentological record of changing conditions. In contrast, optical remote sensing imagery is used extensively to examine contemporary glacial processes, including meltwater dynamics, glacial retreat, calving, and ice accumulation. While paleoenvironmental data from sediment cores and optical remote sensing imagery are rarely used in tandem, they are complementary. Sediment core records are spatially discrete, providing long-term paleoenvironmental proxy data which require assumptions about environment-sediment linkages. Optical imagery offers precise, spatially extensive data to visualize contemporary processes often limited in their temporal extent. We suggest that methodologies which integrate optical remotely sensing with sediment core data allow direct observation of processes interpolated from sedimentological analysis and achieve a more holistic perspective on glacial processes. This integration addresses the limitations of both data sources and can achieve a stronger understanding of glacier dynamics by expanding the spatiotemporal extent of data, reducing the uncertainty of interpretations, and broadening the local analyses to regional and global scales.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".