Mapping the Gulf of Maine: building the link between marine geology and benthic habitats to improve ocean managemen
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.Multibeam sea floor mapping technologies have provided the capability to accurately, and cost effectively, image large areas of the seabed. This imagery provides base maps of sea floor topography and seabed reflectivity from which targeted surveys can be planned to characterize sea floor sediments and associated benthic communities. Over the last decade extensive multidisciplinary surveys have been carried out in the Gulf of Maine on Stellwagen, Browns, German and Georges banks. Other pilot projects in Atlantic Canada and the northeastern USA and around the world have demonstrated the value of integrated sea floor mapping in designating and managing marine protected areas (The Gully, Stellwagen Bank), in identifying offshore hazards such as landslides, in siting offshore structures, cables and pipelines, and in addressing environmental issues such as the routing of outfalls and disposal of dredge materials and mine tailings. To advance the application of these new tools and digital map products, an international scientific team is advocating the development of a mapping strategy to provide the foundation for sustainable ocean management in the Gulf of Maine for the 21st century. Core agencies contributing to the development of this strategy include the Geological Survey of Canada, the United States Geological Survey, Fisheries and Oceans Canada and the National Oceanic and Atmospheric Administration, as well as strong participation from state and provincial agencies. Notably, there is also a strong interest by independent organizations such as the Gulf of Maine Council and the Sloan Foundation (Census of Marine Life) to see this initiative developed further.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".