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Record W3042413035 · doi:10.2495/eid200141

EXPLORING AGRICULTURAL OPPORTUNITIES IN THE CLAY BELT OF ONTARIO, CANADA

2020· article· en· W3042413035 on OpenAlexaffabout
Danielle Robinson, Wayne Caldwell, Sara Epp

Bibliographic record

VenueWIT transactions on ecology and the environment · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAgricultureGeographyAgricultural landAgricultural productivityGovernment (linguistics)SustainabilityTerrainAgricultural developmentAgricultural economicsNatural resource economicsEnvironmental protectionEcologyEconomicsArchaeology

Abstract

fetched live from OpenAlex

There are various clay belts within the Canadian Shield which represent the beds of Pleistocene lakes. The soils and terrain are more suitable to agricultural use than the surrounding shield. The best known of these clay belts lies along the 49th parallel for over 800 km in the Canadian provinces of Ontario and Quebec. The Clay Belt area in northeastern Ontario consists of 10.2 million acres of land, 35% of which is covered in coniferous forest and 28% in mixed forest. The Clay Belt of northeastern Ontario has experienced agricultural growth followed by dramatic decline marked by occasional small reversals; agricultural land in the area peaked in 1951 since which time it has steadily declined although there are recent indications of renewed interest in the abundance of viable agricultural land in the region with the provincial government and agricultural groups in Ontario actively promoting related opportunities. Evolutionary economic geography's emphasis on the significance of history and geography in understanding the development of spatial economies can be applied to the region to better understand how the processes of path creation and path dependence have interacted to shape the geographies of agricultural development in the region. This region has the potential to contribute significantly to agricultural development, food production and in turn benefit local rural economies. The trajectory of agricultural development, however, tells a story of economic, social and environmental barriers that affect sustainability and related environmental impact. This paper will consider the opportunities for agricultural development, while recognizing the need for policy that is sensitive to community needs.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.847
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.036
GPT teacher head0.173
Teacher spread0.137 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2020
Admission routes2
Has abstractyes

Explore more

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