Dry‐lab benchmarking of a structure from motion workflow designed to monitor marine benthos in three dimensions
Bibliographic record
Abstract
Abstract Structure from motion (SfM) has emerged as a popular method for characterising marine benthos (seabed organisms), particularly in clear, tropical waters. However, there are many environmentally sensitive benthic organisms inhabiting temperate waters, including the reef‐forming glass sponges of the north‐east Pacific Ocean. Broader questions are raised, not just about whether SfM is a capable spatial data acquisition and ecological characterisation method in temperate waters; but whether a systematic assessment of capture methods in dry and wet laboratory conditions reveals critical relationships between SfM parameters, data products and their implications for underwater surveys. This paper, the first of two empirical assessments, reports on a series of dry‐lab tests quantifying the impact that lighting, camera type, camera settings and capture strategy have on data accuracy. These tests provide a crucial accuracy baseline for subsequent wet‐lab and field‐based surveys, revealing that photographs captured from a controlled and stable platform produce superior data products. While the measurable differences were small, they may be critical for accurate change detection in temperate environments.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".