MétaCan
Menu
Back to cohort
Record W2519198145

Environmental Impact on Agriculture

2016· article· en· W2519198145 on OpenAlexaboutno aff
Nagaiah

Bibliographic record

VenueResearch and reviews: journal of agriculture and allied sciences · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessLimitingAgricultureNatural resource economicsProduct (mathematics)WelfareQuality (philosophy)Food securityCountermeasureEnvironmental protectionEnvironmental planningEnvironmental scienceEngineeringEconomicsGeography
DOInot available

Abstract

fetched live from OpenAlex

At the point when cultivating operations are economically overseen, they can save and reestablish basic territories, ensure watersheds, and enhance soil wellbeing and water quality. There is a developing pattern among ranchers to bolster welfare-accommodating and practical generation in animals cultivating, with milk created by cows that nibble in fields, unfenced meat and eggs, and natural produce. Pesticides are among Canada's most exceedingly managed items. They are completely investigated by Health Canada to guarantee they can be securely utilized. The combined effects of environmental change will eventually rely on upon changing worldwide economic situations and in addition reactions to neighborhood atmosphere stressors, including ranchers conforming planting designs in light of modified product yields and harvest species, seed makers putting resources into dry spell tolerant assortments, and countries limiting exchange to ensure nourishment security.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.019
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0190.002

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 head0.032
GPT teacher head0.328
Teacher spread0.296 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueResearch and reviews: journal of agriculture and allied sciencesSame topicAgriculture Sustainability and Environmental ImpactFrench-language works237,207