Use of GIS for decision support in coastal zone management in the southwestern New Brunswick portion of the Bay of Fundy
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.The southwestern New Brunswick (SWNB) portion of the lower Bay of Fundy is the location of considerable human activity in its marine waters despite a relatively low human population. Traditionally, fisheries have been the dominant activity in marine waters. Commercial shipping has also been of importance since the earliest days of European settlement. More recently, salmon aquaculture and marine recreational activities have become important, and there is new interest in the energy sector (tidal power generation and liquefied natural gas terminals). The area is also used by endangered species, such as the northern right whale and wild Atlantic salmon. Our first involvement with the use of GIS was for fish health management in the salmon aquaculture industry in SWNB. We used a circulation model and GIS to predict the water-borne spread of diseases, such as infectious salmon anemia, among salmon farms. This work was later used in the delineation of Aquaculture Bay Management Areas for the SWNB salmon farming industry. We also used GIS to conduct a preliminary analysis to determine potential locations for offshore aquaculture in the Bay of Fundy. Using GIS enabled us to overlay available georeferenced information on activities, resources, and other issues to determine where overlaps occur, and where the potential for locating offshore aquaculture would likely cause the least negative interaction and potential for conflict. We can also use GIS with oceanographic data and models to predict which areas are technically best suited for certain activities, such as aquaculture or tidal power. The presentation will highlight some of our applications and experiences with taking this approach to Decision Support.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".