Mapping socio-ecological values: the use of geospatial tools to make informed decisions on the marine and coastal management areas surrounding Gros Morne National Park, Newfoundland
Bibliographic record
Abstract
Marine and coastal environments are highly complex integrated systems. While it is recognized these aquatic environments offer valuable ecosystem services, there is a paucity of information on how these systems are structured and how they function. Moreover, there are few tools available to assist in the management of these natural resources. Marine and coastal environments are not only important to the stability of the ocean but also to the socio-cultural, ecological and economic well-being of coastal communities. Many important biological areas are vulnerable to “agents of change” which include but are not limited to, commercial fishing, oil and gas activities, tourism and aquatic invasive species (green crab and membranipora specifically) and, climate change. This study will use expert informed GIS (xGIS) as a management tool to highlight the socio-ecological areas of importance and perceived impact in the coastal and marine areas surrounding Gros Morne National Park, western Newfoundland, Canada. This research used a family of decision-making protocols to promote effective stakeholder participation, allowing exploration and evaluation of multiple attributes where cost benefit analysis was inappropriate. The geospatial tool created for this study will serve as a management tool that can help: 1. identify geospatial hotspot areas of importance and impact from various ‘agents of change’ in the coastal and marine management areas surrounding the Gros Morne Region of western, Newfoundland; 2. construct a tool that can be used to aid in the creation of responsible marine plans for Newfoundland and areas bordering the Gulf of St. Lawrence and; 3. identify socio-ecological and justified areas valued for protection under a National Marine Conservation Area around Gros Morne National Park, Newfoundland.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".