Farmers’ willingness to plant trees on marginal agricultural land in Canada’s grain belt
Bibliographic record
Abstract
Climate change has been one of the major global environmental concerns to date. Its seriousness supported by many scientists around the world prompted the vast majority of countries to sign the Kyoto Agreement on climate change. In this document Canada committed to a six percent reduction below 1990 level of carbon dioxide emissions by the 2008-2012 commitment period. Canada has expressed its intention to use its extensive land base as a carbon sink by planting trees. However, no data are available on precisely how much of the land can be converted to trees and at what cost. This thesis uses a survey of farmers in the grain-belt region of Canada to investigate the costs of planting trees on marginal agricultural land and estimate the amount of land available for tree planting. The survey proposes a random bid to each farmer for accepting a particular tree-planting contract. Farmers' answers are analyzed using a bivariate probit model that provides an estimate of the mean willingness to accept for each farmer. Regressing the number of acres made available at this bid on the difference between the bid and the mean willingness to accept results in a supply type of schedule that provides a general estimate of the potential for tree planting in Canada for climate change mitigation purposes. The thesis concludes that Canada can rely on offsetting its emissions of carbon dioxide by means of biological mitigation only to a limited extent due to the high cost of compensation to landowners for their land.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".