A data-driven approach to understanding esker morphogenesis
Bibliographic record
Abstract
Eskers are ubiquitous features on previously glaciated landscapes, recording the configuration and dynamics of the channelized meltwater system. Studies of esker composition and form have resulted in a variety of genetic interpretations surrounding the ice, water, and sediment characteristics under which they may develop. However, issues of apparent equifinality currently limit the usefulness of eskers for reconstructing broad-scale glacial hydrology. Although some authors have attempted to asses esker morphogenesis, previous studies are limited by their small sample size and/or use of qualitative morphometric indices.This project aims to explore whether eskers have a distinct morphogenetic signature using data science techniques. Published research has been mined for empirical studies of esker composition and structure. These data were compiled into a database summarizing the genetic interpretations commonly invoked for eskers (e.g., depositional environment, meltwater flow regime) as well as the supporting evidence for such inferences (e.g., sedimentary logs). Semi-automated methods will be tested to map eskers from high resolution (1-2 metres) LiDAR digital terrain models and to extract their morphometry. A range of planform- and profile-scale morphometric indices will be employed and new indices that can more precisely quantify esker morphometry will be developed.The resulting highly-dimensional dataset can be analyzed using machine learning techniques in order to assess the relationships between sedimentologic, morphometric, and genetic variables. Preliminary results from database development and analysis will be presented and methodological concerns will be discussed.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".