A study into marine landscapes applied to habitat mapping
Bibliographic record
Abstract
In recent years there have been many new seabed-mapping programmes carried out around the \nworld using the latest data acquisition techniques. The need for these maps is driven by the \nrecognition that an ecosystem-based approach to the management of national Exclusive \nEconomic Zones (EEZs), as required by the Convention on Biological Diversity (CBD) adopted \nat the Rio Earth Summit in 1992, requires both detailed bathymetry and maps of the physical \nproperties of the sea floor. \nIn Europe, the implementation of the CBD is through the Habitats and Birds Directives, which \nrequire the identification of Special Areas of Conservation (SACs) and Special Protected Areas \n(SPAs). A network of SACs and SPAs will be set up across Europe known as Natura 2000. In \naddition, the International Council for the Exploration of the Seas has developed the concept of \nthe ecosystem-based approach in the context of fisheries management, subsequently adopted by \nthe European Union in its review of the Common Fisheries Policy; the World Wildlife Fund for \nNatures’ marine policy has developed the concept of Marine Protected Areas (MPAs); the \nOSPAR Convention for the Protection of the Marine Environment of the North East Atlantic is \nalso working to promote networks of MPAs and Ecological Quality Objectives (EcoQOs). \nIn the UK, the Department of Environment, Fisheries and Rural Affairs (DEFRA) major reports \non Marine Stewardship,’ Safeguarding our Seas: A Strategy for the Conservation and \nSustainable Development of our Marine Environment’ published in 2002 and followed by a \nconsultation paper ‘ Seas of Change’, form the basis for developing a practical application of the \necosystem-based approach. The DEFRA Review of Marine Nature Conservation (RMNC) \nproduced an interim report in 2001, which recommended that a pilot scheme at a regional scale \nto test a proposed framework for nature conservation. This has led to the Irish Sea Pilot Study \nmanaged by the Joint Nature Conservation Committee (JNCC), which has adopted the concept \nof ‘marine landscapes’, first developed in Canadian waters, based on geophysical features \nrecognising that their importance in determining the nature of biological communities. \nAs a result of these national and international initiatives, a number of habitat classification \nschemes have been introduced in different parts of the world. Since 2001, a group of geologists \nwith interests in the application of geological data to habitat mapping have met each year to \npresent their views and mapping programmes and to discuss their ideas with scientists from other \ndisciplines, mainly biologists and oceanographers. The GeoHab (Geological Mapping of \nHabitats for Marine Resources and Management) group have provided the impetus for this \nreview of BGS geological data in the context of habitat mapping classification schemes, and \nproposes ways in which our BGS data may be applied automatically, within a Geographical \nInformation System, to the selection of relevant sources of information.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".