A comparison of two techniques for the rapid assessment of marine habitat complexity
Bibliographic record
Abstract
Summary Monitoring and assessment of the status and distribution of marine seabed habitats is needed to support existing and emerging environmental policy commitments. Traditional monitoring of habitats and associated species using grabs and trawls is costly and labour intensive and might usefully be complemented by cheaper and more readily automated methods that can be used at higher frequencies and/or on larger spatial scales. We develop and apply two methods to measure seabed habitat complexity and demonstrate how they can be used to describe impacts (e.g. fishing gear impacts) and monitor recovery. The first method relies on the analysis of deviations in a laser line projected on the seabed. The second method is based on the pixel value distribution in seabed photographs. We use both methods to quantify the complexity created by different substrates and habitat‐forming species and to establish links between habitat complexity and faunal diversity (richness) and abundance. The habitat complexity index calculated with the laser line method provided a reliable index of complexity across a range of habitat types, showing a monotonic increase with coarseness of the substratum and the abundance of sessile epifauna. Pixel value distributions in the photographs did not reflect the increase in complexity due to sessile epifauna but only reflected substratum differences. Results suggested that the laser line method would be suitable for monitoring the effect of disturbance on habitats ranging from gravelly sands to rock, and their subsequent recovery. The photographic method would be better suited to assessing complexity and heterogeneity of the substratum. Both methods complement conventional biological sampling and can be used at higher frequencies and/or on larger spatial scales per unit cost. The laser line method has considerable potential to support demands for frequent monitoring of seabed habitats and human impacts at a range of spatial scales. It is less costly and labour intensive than existing approaches and can be deployed from vessels of many sizes.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".