Analyzing Dune Foreset Cyclicity In Outcrop With Photogrammetry
Bibliographic record
Abstract
Abstract: Collection of dune foreset thicknesses from outcrop can provide valuable sedimentological information. One means of inferring a tidal origin of a sedimentary deposit is to use variations in dune foreset thickness and analyze those data to identify periodicity of sedimentation rates. Measurements of dune foreset thickness from outcrop are typically collected using photographs, or in the case of larger intervals, a photomosaic: perspective distortion that results from the projection of a three-dimensional outcrop onto two dimensions is unavoidable in this type of dataset. This distortion will affect any collected thickness measurements, and ultimately the final results and interpretation of the dataset. Herein, we present an alternative method that uses 3D photogrammetric techniques to mitigate errors and perspective distortion of a dataset using a case study from the McMurray Formation in Alberta, Canada. Collecting thickness measurements from 3D photogrammetry models yields greater precision and reduces noise in the dataset, allowing a stronger interpretation of the data.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".