A subtidal marine ecological classification system to represent species diversity and distribution patterns in the Maritimes region
Bibliographic record
Abstract
The need to develop a Hierarchical Marine Ecological Classification System (HMECS) for classifying the structure and distribution of Canada’s marine biota and habitats at multiple spatial scales has been recognized regionally, nationally, and internationally. An HMECS will help ensure that all habitats, communities, and ecosystems are effectively represented in Marine Protected Area (MPA) networks, and ensure that a structured approach is used to consider biodiversity at local, regional, and basin-wide scales during other marine spatial planning and oceans management applications. A conceptual framework for an HMECS was identified for the Pacific Region, and then harmonized for applicability between the Pacific and Maritimes Regions. The harmonized HMECS contains 11 levels, and approaches for populating Levels 4–8, below the Bioregion level, are discussed. The conceptual framework was applied in Pacific and Maritimes Regions to provide a systematic and spatially-explicit classification of ecosystems at multiple scales. A database of spatially-referenced information for identifying and locating key ecological properties was developed as part of this exercise. We also developed a set of spatially referenced information that can be integrated with other data layers (e.g., social, economic). These outputs are intended to support marine spatial planning and conservation in both the Pacific and Maritimes Regions, particularly the design of MPA networks. This paper was presented and peer-reviewed at the September 29–October 2, 2015 zonal meeting on Evaluation of Hierarchical Marine Ecological Classification Systems for Pacific and Maritimes Regions held in Nanaimo, British Columbia. It describes the application of the classification in the Maritimes Region, with a focus on benthic ecosystem attributes two levels below the Bioregion level (Biophysical Domains and Geomorphic Units), including a separate classification for coastal areas. The environmental data used in the application were weighted by previous biological analyses in the region. Methods were proposed for populating the Biotope Level. These classifications will be used to help achieve the representativity criterion for MPA Network design in the Region.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".