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Record W4243878836 · doi:10.32920/ryerson.14644635.v1

Considering the Value of "Marginal" Agricultural Lands : Planning Analysis of Agricultural Resource Land Protection in Ontario

2021· preprint· en· W4243878836 on OpenAlexaboutno aff
John O’Neill

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicAgroforestry and silvopastoral systems
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureAgricultural landResource (disambiguation)Land useNatural resource economicsAgricultural productivityEnvironmental planningAgricultural economicsLand managementBusinessLand-use planningMarginal landEnvironmental resource managementGeographyEconomicsEngineeringCivil engineeringComputer scienceArchaeology

Abstract

fetched live from OpenAlex

Agricultural land resources are an essential element required to sustain agricultural production. While the Province of Ontario has implemented policies that aim to protect these lands from other types of development, this finite resource continues to diminish as the demand for food continues to grow. At this time the Province is undertaking a review of existing policies related to matters of provincial interest, including agriculture and therefore presents an important opportunity to re-evaluate the policies, in particular as it relates to what lands qualify as prime agricultural land worthy of protection. Historical and emerging agricultural practices have demonstrated that Canada Land Inventory (CLI) Class 4 soils can be productive. The report examines the potential merit of expanding the existing defining criteria of prime agricultural land from just CLI Class 1, 2, and 3 soils to also include CLI Class 4 and attempts to demonstrate the impact this would have on agricultural land use planning in Ontario. To help demonstrate a site specific evaluation of a portion of Peterborough County has been conducted to provide a visual representation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.755

Codex and Gemma teacher scores by category

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

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 head0.037
GPT teacher head0.220
Teacher spread0.182 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2021
Admission routes1
Has abstractyes

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