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Aggregates & Agriculture: Understanding the Impacts of Aggregate Production on Agriculture and Identifying Mitigating Strategies

2020· article· en· W4408459983 on OpenAlexfundvenueaboutno aff
Jeff Reichheld and Emily Hehl

Bibliographic record

VenueRural Review Ontario Rural Planning Development and Policy · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsnot available
FundersMinistry of Agriculture, Food and Rural AffairsOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsAgricultureProduction (economics)Aggregate (composite)Natural resource economicsAgricultural productivityEnvironmental scienceBusinessAgricultural economicsEconomicsGeographyMicroeconomicsMaterials scienceNanotechnology

Abstract

fetched live from OpenAlex

Aggregate extraction is an important economic stimulus for many rural Ontario communities. However, these operations can significantly alter the local landscapes and are often considered a nuisance, especially to local agriculture. Aggregate resources can only be extracted from where they occur, and thus frequently disrupt agricultural production. It is therefore important to understand potential impacts on adjacent and nearby farms. This research investigates the social, environmental, and economic impacts of aggregate extraction on adjacent agricultural activity by examining the perceived relationship between the two industries that are often perceived as existing in conflict. This research also examines land use and policy as they pertain to aggregate extraction activity to understand the potential developmental implications of this relationship. Ultimately, this research is expected to discover strategies for mitigating land use conflicts, as well as to offer strategies for managing the necessary local relationships.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.959

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.059
GPT teacher head0.272
Teacher spread0.214 · 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
Published2020
Admission routes3
Has abstractyes

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