Atlantic Coastal Island Database Presentation for Coastal Zone Canada 2023 Conference, Victoria, B.C.
Bibliographic record
Abstract
Coastal islands in Atlantic Canada are known for their intactness, diversity of habitats, and significant biological and geological features. They provide isolated refuges for many species, areas of high intertidal productivity, unique climatic conditions that support boreal plant and bird communities, and act as buffers against coastal storm impacts from climate change. Many key ecosystem services are a result of interactions between a combination of habitat types within coastal island seascapes. Despite their value, biophysical and anthropogenic information at the island scale is missing throughout much of Atlantic Canada. Given the lack of stored information, government departments and conservation organizations are ill-equipped to make structured, evidence-based decisions about the acquisition and protection of coastal islands, and the valuation of those ecosystem functions and services. To address the gap in geospatial information and improve accessibility for island ecosystem valuation, we are developing an Atlantic Coastal Island (ACI) Database to house biophysical and anthropogenic geospatial data on islands in the Atlantic region. The development of the database employs a geospatial framework using a staged approach. In stage one we improved upon an existing geospatial database built by the Kespukwitk/South West Nova Scotia Coastal Islands Working Group (NSNT, 2021). Among other products, we generated NDVI, LiDAR and topographical layers for 3,921 coastal islands in Nova Scotia. In subsequent stages, we will aim to duplicate the expanded Island Geospatial database that now exists for Nova Scotia to other provinces, likely beginning with Newfoundland. This presentation will offer an overview of what coastal island information now exists in Nova Scotia and outline anticipated products for Newfoundland, PEI and New Brunswick. Coastal island seascapes are synergistic land-sea systems and an important element in securing representative protected area networks. We expect the creation of a geospatial database of islands in the Atlantic Region will support structured, evidence-based conservation decisions as well as supporting other research objectives.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.317 | 0.218 |
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".