Farmland conversion to non-agricultural uses in the US and Canada: current impacts and concerns for the future
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it