Geophysical approaches to the classification, delineation and monitoring of marine habitats and their communities
Bibliographic record
Abstract
Abstract If marine environments are to be systematically protected from the adverse effects of human activities, then identification of the types of marine habitats and the communities they contain, and delineation of their boundaries utilizing a consistent classification is required. Human impacts on defined communities can then be assessed, the ‘health’ of these communities can be monitored, and marine protected areas can be designated as appropriate. Schemes to classify habitats at local and regional scales, according to their geophysical properties, may identify different factors as determinants, and/or use them in different sequences in a hierarchical classification. We examined the reasons for these differences in local and regional applications of a global concept, and argue that a common set of factors could be applied in a defined and defensible sequence to produce a common hierarchy of habitat types among geographic regions. We show how simple mapping and GIS techniques, based on readily available data, can lead to the identification of representative habitat types over broad geographic regions. We applied a geophysical framework first to the entire Canadian coastline and second to the Scotian Shelf of Atlantic Canada to establish broad scale marine natural regions and ‘seascapes’, respectively. This ecosystem level approach — which defines representative habitat types — is a fundamental prerequisite for many purposes. It can form the basis for further analyses including: definition of community types from habitat — community relationships; evaluation of the potential roles of focal species in marine conservation; evaluation of candidate marine protected areas; definition of unaffected reference areas against which the effects of human activities can be gauged; guidance for water quality monitoring studies; management of marine resources. Copyright © 2003 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".