Modeling benefits from nature: using ecosystem services to inform coastal and marine spatial planning
Bibliographic record
Abstract
People around the world are looking to marine ecosystems to provide additional benefits to society. As they consider expanding current uses and investing in new ones, new management approaches are needed that will sustain the delivery of the diverse benefits that people want and need. An ecosystem services framework provides metrics for assessing the quantity, quality, and value of benefits obtained from different portfolios of uses. Such a framework has been developed for assessments on land, and is now being developed for application to marine ecosystems. Here, we present marine Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST), a new tool to assess (i.e., map, model, and value) multiple services provided by marine ecosystems. It allows one to estimate changes in a suite of services under different management scenarios and to investigate trade-offs among the scenarios, including implications of drivers like climate. We describe key inputs and outputs of each of the component ecosystem service models and present results from an application to the West Coast of Vancouver Island, British Columbia, Canada. The results demonstrate how marine InVEST can be used to help shape the dialogue and inform decision making in a marine spatial planning context.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".