Using the benthoscape approach in an offshore marine protected area - a case study on St. Ann's Bank (Atlantic Canada)
Bibliographic record
Abstract
The establishment of multibeam echosounders (MBES) as a mainstream tool in ocean mapping has facilitated integrative approaches towards nautical charting, benthic habitat mapping, and seafloor geotechnical surveys. The inherent bathymetric and backscatter information generated by MBES enables marine scientists to present highly accurate bathymetric data with a spatial resolution closely matching that of terrestrial mapping. A range of post-processing approaches can generate customized thematic seafloor maps to meet multiple ocean management needs, thus extracting maximum value from a single survey data set. Applying objective segmentation methods when analyzing backscatter data collected using a variety of multibeam echo sounder systems from a study can pose challenges due to the non-calibrated nature of the sounders. The lack of backscatter calibration, due for example, to system-specific settings and characteristics of the water column during acquisition, yield relative rather than absolute values. This hinders the creation of habitat maps if multiple, non-overlapping surveys are available. Here, we first describe an approach using object-based image analysis and supervised classification to combine 4 non-overlapping and uncalibrated MBES coverages to form a seamless habitat map on St. Ann's Bank (Atlantic Canada), a proposed marine protected area hosting a diversity of benthic habitats. The benthoscape map was produced by analysing each coverage independently with supervised classification (kk-nearest neighbour) of image-objects based on a common suite of 6 benthoscape classes (determined with 4164 ground-truthing photographs at 61 stations, and characterized with backscatter, bathymetry, and bathymetric position index). Manual re-classification based on uncertainty in membership values to individual classes - especially at the boundaries between coverages - was used to build the final benthoscape map. We then propose how this thematic map can be used to support ocean management, in particular by examining the potential role of organism-landscape relationships when framing conservation strategies. Given the costs and scarcity of MBES surveys in offshore marine ecosystems - particularly in large ecosystems in need of adequate conservation strategies, such as in Canadian waters - developing approaches to synthesize multiple datasets to meet management needs is warranted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".