Graphical Shading Logs: An Improved Approach for Collecting High Resolution Sedimentological Data at Contaminated Sites
Bibliographic record
Abstract
Abstract Predicting contaminant transport in groundwater requires an accurate representation of the subsurface geology controlling the spatial distribution of hydrogeologic parameters. Developing accurate geological models for sedimentary systems relies on quality sedimentological data collected from cores. Standard logging forms used to collect data from cores create a persistent data gap in hydrogeology because they hinder efficient collection of high‐quality sedimentological data. These logging forms require time‐consuming text descriptions of sedimentological characteristics and often result in inconsistent, poorly resolved data insufficient to support realistic geological models. We describe a graphical approach to core logging, the graphical shading log, that facilitates rapid, accurate capture of sedimentological data and a complementary database to store the raw data and interpretations. The visual format of the graphical shading log provides a roadmap of the parameters to log and their possible values, helping to ensure accurate and consistent data collection by loggers with a range of experience. Examples from sites with contaminated groundwater in glaciogenic sediments and siliciclastic and carbonate bedrock show how data from the graphical shading logs improved geological interpretations, supported the design of high‐resolution multilevel systems needed to collect minimally blended hydrogeologic data, and helped to more accurately delineate hydrogeologic units. The format of the graphical shading log and complementary database are designed to be customizable and transferable between hydrogeologic settings providing a new tool to advance geological data collection and management. Improved sedimentological data and insight are critical inputs for process‐based conceptual site models needed to effectively manage contaminant plumes in the subsurface.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".