A"Delphi exercise"as a tool in Amazon rainforest valuation
Bibliographic record
Abstract
The Amazon rainforest, the world's largest and most biodiverse, represents a global public good of which 15 percent has already been lost. The worldwide value of preserving the remaining forest is today unknown. ADelphiexercise was conducted involving more than 200 environmental valuation experts from 36 countries, who were asked to predict the outcome of a survey to elicit willingness to pay for Amazon forest preservation among their own countries'populations. Expert judgments of average willingness-to-pay levels, per household per year, to fund a plan to protect all of the current Amazon rainforest up to 2050, range from $4 to $36 in 12 Asian countries, to near $100 in Canada, Germany, and Norway, with other high-income countries in between. Somewhat lower willingness-to-pay values were found for a less strict plan that allows a 12 percent further rainforest area reduction. The elasticity of experts'willingness-to-pay assessments with respect to own-country per capita income is slightly below but not significantly different from unity when results are pooled across countries and income is adjusted for purchasing power parity.
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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 | 0.307 | 0.261 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".