A new video survey method of microtopographic laser scanning (MiLS) to measure small‐scale seafloor bottom roughness
Bibliographic record
Abstract
A novel video survey method measures small‐scale seafloor bottom roughness in fragile and deep‐sea habitats called microtopographic laser scanning (MiLS). Using a controlled submersible platform, an attached downward‐ facing video camera with a single optical laser can return imagery to detail the bottom profile at a resolution of ~1–2 cm. The method compares the position of the underlying substratum and laser dot between successive video frames to determine distance traveled in the forward direction and substratum height. The video imagery is processed using photogrammetry to calculate small‐scale topography (horizontal and vertical axes). MiLS is adaptable for most aquatic habitats as it can be executed using any platform that can move forward with a constant slope over the desired transect. Traditional techniques of measuring small‐scale roughness are largely restricted to easily accessible habitats and often yield measurements that are relative and not comparable among different habitats and studies. Quantifying roughness in ways that permit comparisons is critical to understanding effects of bottom roughness and would benefit many fields of aquatic science. With its versatility, ability to access remote locations and output of quantified measurements, MiLS has the potential to fill this need. It is also likely this method will be useful in subaerial habitats such as wetlands. Here, we describe the MiLS equipment, theory, and method in detail, and then demonstrate its application in a lab trial and in a field study in a deep‐sea (≤450 m depth) sponge and coral habitat where its high resolution, accuracy, and precision is made evident.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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".