Integrated Seafloor Mapping: A Tool for Sustainable Management of Our Offshore Lands
Bibliographic record
Abstract
Globally coastal and ocean environments are coming under increasing pressure from resource development. Maritime countries are struggling to develop the knowledge base and a sound management framework for sustainable management of coastal and offshore resources. Consequently, competition for use of the seabed is often unresolved, hazards are overlooked, unique habitats are not protected, and fisheries collapse is common. In the terrestrial environment, decision making is supported through integrated resource management; where remote sensing and mapping have made knowledge acquisition routine. In marine environments, remote sensing can only be used in the littoral zone and until recently visualizing the sea floor in deep water at high resolution has not been possible. However, the development of multibeam seafloor mapping has provided the first opportunity to accurately map the shape of the seabed, the sediment cover and associated benthic habitat. The knowledge base can now be provided to support sustainable, integrated ocean management and to complete the mapping of our offshore lands. Several countries, including Canada, are in the process of developing strategies to support national seafloor mapping programs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".