Farmland conversion to non-agricultural uses in the US and Canada: current impacts and concerns for the future
Bibliographic record
Abstract
Conversion of farmland to non-agricultural uses presents a challenge to future food production and ecosystem services in US and Canada. Expansions of housing, transportation, industry, retail sales, schools and other developments are driving land out of farming. In the US there is annual conversion of 500,000 ha away from food and fibre production systems. Coupled with 1% annual population increase, this will reduce today's 0.6 ha per person to 0.3 ha by 2050. Canada has more land and smaller population, but farmland losses are occurring in fertile areas near coasts and in level valleys where highest quality land is located. Current rates of increase in agricultural productivity will not compensate for this land loss. Compared to US, there are more specific tools and legislation at the provincial level in Canada that provide opportunities for controlling sprawl. Important in both countries is general lack of awareness and concern about loss of productive farmland, a situation that could be improved through education. Stimulating collective understanding of this growing problem and providing viable solutions could provide the basis for national policy strategies to promote and assure sustainable food systems for the future and enhance the capacity to maintain vital 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".