B.C. marine energy resource atlas and decision support system
Bibliographic record
Abstract
We present a web-based geospatial decision support system (DSS) to facilitate MRE development feasibility investigations in coastal British Columbia (B.C.), Canada. The DSS, known as the B.C. Marine Energy Resource Atlas, hosts an extensive database of geo-spatial information pertinent to MRE development. The data are integrated via multi-criteria decision analysis (MCDA) to identify development hotspots. Previous MRE hotspot identification studies typically focus on visual presentation, employing traditional Geographic Information Software (GIS) to support MCDA. The DSS discussed herein is primarily focussed on visual exploration such that users are able to interact with the data and perform custom interrogations. In comparison to traditional GIS software, the web-based application enhances user-accessibility and alleviates reliance on the users’ experience with GIS. Furthermore, we present a novel vector-based approach to MCDA not used in previous MRE hotspot identification studies. Previous studies typically employ MCDA using a fixed-resolution raster-based approach which introduces a trade-off between nearshore accuracy and computation time. The approach presented herein permitted dynamic resolution throughout the study domain diminishing this trade-off.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".