Evaluating the 3D Integrity of Underwater Structure from Motion Workflows
Bibliographic record
Abstract
Abstract Structure from motion (SfM) is an accessible and non‐intrusive method of three‐dimensional (3D) data capture popular for tropical coral reef surveying. In the north‐east Pacific Ocean, where there are many environmentally sensitive benthic organisms whose morphology and function are equally important, SfM surveys are less commonly studied. Temperate waters pose unique challenges to SfM workflows, which must be systematically unpacked to understand their impact on data quality and veracity. This uncertainty raises broader questions concerning SfM as a spatial data‐acquisition and ecological characterisation method in temperate waters, and whether a systematic workflow assessment reveals vital relationships between SfM implementation parameters, 3D data products and their implications for underwater SfM surveys. This paper, the second of two empirical assessments, reports on a series of wet‐lab and dryland tests quantifying the impact that temperate waters, underwater cameras, and photograph quantity and configuration have on SfM accuracy. These tests provided crucial accuracy benchmarks informing subsequent field‐based surveys and revealed that underwater SfM workflows can generate highly accurate 3D models in temperate waters.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".