Mapping Marine Ecosystem Service Values and Threats
Bibliographic record
Abstract
"Recognizing that local knowledge and values should play a prominent role in natural resource decision-making, we tested a semi-structured interview protocol to solicit the verbal articulation, spatial identification and a quantitative measure of local monetary values, non-monetary values and threat intensity associated with marine ecosystem services. Ecosystem services are the ecological processes through which nature provides benefits to people. Interviewees identified and characterized a wide range of ways in which they value marine ecosystems in the Regional District of Mount Waddington in British Columbia, Canada. This research is intended to inform an ongoing marine spatial planning process in this region. A total of 30 semi-structured interviews were conducted based on non-proportional quota sampling to target interviewees with a variety of marine-related occupations who live across the district. There was significant spatial overlap among all three pair-wise comparisons of monetary values, non-monetary values, and threat intensity values. Employment in salmon aquaculture correlated with the perception that the ocean does not face environmental threat associated with this industry. A minority of respondents refused to participate in the spatial and quantitative components of this research, yet all verbally identified the importance of marine ecosystems. The results of this research and the methods could complement deliberative processes to enable decision makers to more fully consider stakeholder???s non-monetary values and threats associated with ecosystem services."
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".