Dry‐lab benchmarking of a structure from motion workflow designed to monitor marine benthos in three dimensions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it