3D MODELLING IN TEMPERATE WATERS: BUILDING RIGS AND DATA SCIENCE TO SUPPORT GLASS SPONGE MONITORING EFFORTS IN COASTAL BRITISH COLUMBIA
Bibliographic record
Abstract
Abstract. Structure-from-motion (SfM) has emerged as a popular method of characterizing marine benthos in tropical marine environments and could be of tremendous value to glass sponge monitoring and management efforts in the Northeast Pacific Ocean. However, temperate marine environments present a unique set of challenges to SfM workflows, and the combined impact that cold, dark, and turbid waters have on the veracity of SfM derived data must be critically evaluated in order for SfM to become a meaningful tool for ongoing glass sponge research. This paper discusses the design, development, testing, and deployment of an innovative underwater SfM workflow for generating high-resolution 3D models in temperate marine environments. This multi-phase research project (dry-lab, wet-lab, and field), while possibly seen as unconventional, was designed to innovate in two ways. First to build an operational data capture platform to support low-cost SfM-based seafloor surveys. And second, to enable systematic isolation and evaluation of SfM data capture parameters and their implications for representational veracity and data quality. This paper reports the challenges and outcomes from a series of field surveys conducted in Howe Sound, BC, one of which serves as the first of two data sets in a temporal analysis of 3D morphometric change. This research demonstrates that accurate, high-resolution morphometric characterization, of all benthic species and habitats, is dependent on a range of equipment, procedural, and environmental variables. It is also intended to share our applied problem-solving path to successful 3D capture, backed up by robust data science.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".