A data-driven approach to understanding esker morphogenesis
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
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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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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