Digital re‐evaluation of down‐dip channel‐fill architecture in deep‐water slope deposits: Multi‐scale perspectives from UAV‐SfM
Bibliographic record
Abstract
Abstract Recent advances in sea floor measurements and modelling have revealed new insight into submarine channel processes; however, understanding how these short‐term perspectives influence long‐term evolution of submarine channels has been limited by the difficulty in linking processes to products in the stratigraphic record. Outcrops present opportunities to characterise the detailed internal architecture of deep‐water channel fills over a wide range of timescales, but obtaining observations is compounded by challenges in outcrop accessibility and perspective along broad exposures. To demonstrate the potential value of modern remote sensing techniques in supplementing fieldwork, an extensive dip‐oriented outcrop exposure of Cretaceous deep‐water channel deposits was re‐evaluated using a 3D digital outcrop model generated from uninhabited/unmanned aerial vehicle photogrammetry. Results confirmed previous field‐based documentation of depositional element‐scale stratigraphic architecture, but also revealed nuanced internal detail that was not captured from field‐based perspectives alone. Subtle internal channel‐fill architecture, including discontinuous sandstone wedges and the interpreted stratigraphic products of upslope‐migrating bedforms, are also recognised. This study demonstrates the sedimentary detail that can be uncovered by integrating conventional field‐based approaches limited by viewable scale, perspective, and/or accessibility, with emerging remote sensing techniques. The unmanned aerial vehicle photogrammetry approach used here provides valuable supplemental data in the investigation of deep‐water channel system deposits and has the potential to overcome inherent challenges in outcrop mapping for numerous applications.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".