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Record W2339003385 · doi:10.1139/cjas-2015-0118

An economic analysis of resting versus nonresting perennial pastures during the critical acclimation period

2016· article· en· W2339003385 on OpenAlexaffvenue
Stefanie Fryza, Jared G. Carlberg, Mohammad Khakbazan, Clayton Robins, H. C. Block, John Huang, Obioha N Durunna

Bibliographic record

VenueCanadian Journal of Animal Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsSaskatchewan Ministry of AgricultureAgriculture and Agri-Food CanadaUniversity of Manitoba
Fundersnot available
KeywordsGrazingPerennial plantAgronomyProductivityBiologyMedicago sativaBeef cattleEconomic analysisAnimal scienceEconomics

Abstract

fetched live from OpenAlex

Economic evaluations were carried out on stochastically simulated production systems using data from a grazing systems’ trial, which evaluated the impact of resting (i.e., grazing cessation) alfalfa (Medicago sativa L.) – bromegrass (Bromus riparius Rehm. ‘Fleet’) and grass-only (G) pastures during the critical prefrost period on beef cattle (Bos taurus) productivity. Grazing systems were compared on the basis of calculated net present value of returns analysis. Lower production costs through reduced fertilization requirements resulted in alfalfa–grass (AG) preference over G pastures, while returns from increased calf sale weights resulted in nonrested systems being preferred over rested systems. However, low presence of alfalfa in the AG pastures likely limited the potential for resting pastures to improve production through increased alfalfa persistence. This evaluation found nonrested grazing of AG pastures to be preferred over other treatments on the basis of increased returns and reduced risk.

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)0.003
metaresearch head score (Gemma)0.003
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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.035
GPT teacher head0.283
Teacher spread0.248 · 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

Citations1
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Animal Science→Same topicRuminant Nutrition and Digestive Physiology→French-language works237,207→