Bibliographic record
Abstract
Public concern of the value of environment quality has risen over the past three decades and numerous policies, programs and strategic plans have been developed to address damage to the quantity and quality of environmental attributes. The adoption of agricultural beneficial management practices (BMPs) by producers can result in increased environmental benefits and/or decrease the negative environmental impacts from certain agricultural activities. In Canada, farmers have been encouraged to adopt BMPs through government payments that are designed to partially offset the costs of BMP adoption on their land. The purpose of this study is to develop estimates of the social value of environmental improvements caused by the adoption of BMPs by farms in Manitoba. A contingent valuation method (CVM) is used to estimate the social value of improvements in water clarity, water odour, water quantity (flood reduction), and recreation and fish habitat using two sample population; 1) South Tobacco Creek (STC) watershed area in south western Manitoba, 2) Ag Days Farm show in Brandon, Manitoba. Heckman selection models (Probit and OLS regressions), are used to estimate respondent’s willingness to pay for some environmental quality improvements. The results suggest that society ascribes positive value to the selected environmental quality improvements with water quantity (flood reduction) attributed the highest value.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".