Development of low-cost image mosaics of hard-bottom sessile communities using SCUBA: comparisons of optical media and of proxy measures of community structure
Bibliographic record
Abstract
Underwater image-based sampling procedures, using SCUBA, are compared using imagery collected from a temperate hard-substratum community. The effectiveness of a low-budget, high-resolution image mosaicing technique is assessed by comparing the relative efficiency of data collection, extraction and analysis among sampling procedures. In addition, a manipulative experiment tested whether the sampling procedures could detect the physical removal of 10% of the reef community. Overall, four factors were explored within the data: data collection media (stills and video), cover and community composition estimation techniques (visual cover estimation, frequency of occurrence and point extraction), change detection (pre- and post-impact) and depth (8, 14, 18 and 22 m). Stills imagery sampled the reef community at a higher image resolution than the video imagery, which enabled identification of more species and less-conspicuous benthic categories. Using the visual cover estimation technique, the stills imagery also had the greatest benefit in terms of efficiency and species identification. However, the experimental impact was detected using only the point extraction technique. The recommendations are that: (1) the image mosaicing technique is applied to fixed-station monitoring; (2) the point extraction technique be used for efficient and cost-effective monitoring at coarse taxonomic resolutions; and (3) survey depths remain constant over the duration of hard-substratum community monitoring.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".