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Data to explore circular manureshed management in beef supply chains of the United States and western Canada

2023· dataset· en· W6958023237 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2023
Typedataset
Languageen
FieldComputer Science
TopicIntelligent Tutoring Systems and Adaptive Learning
Canadian institutionsnot available
Fundersnot available
KeywordsGrazingAgroecosystemFeedlotBeef cattleManureHaySustainabilityManure managementFencing

Abstract

fetched live from OpenAlex

Circular management of beef supply chains holds great promise for improving sustainability from grazing agroecosystem to dinner plate. In the United States and Canada, one approach to circularity entails transporting manure nutrients from cattle produced in feedlots back to the grazing agroecosystems where they originated to enrich haylands for further grazing cattle production. We provide data to assess this strategy centered around three grazing agroecosystems: Florida, New Mexico, and the provincial assemblage of Manitoba, Saskatchewan, Alberta, British Columbia. We describe four datasets that can be used to estimate the potential nutrient utilization of hay fed to grazing cattle in the three grazing agroecosystems and the magnitudes of feedlot manure nutrients available for transport back to them. We found that although biogeography and management differ among the three grazing agroecosystems, the hay allocated for grazing cattle represented approximately 65% of the total harvested hay produced per agroecosystem after accounting for harvest losses, and that on average all three areas exported about 450,000 cattle annually for feedlot, pasture, and slaughter to states across the US. Although we highlight only three grazingland settings, our approach relies on methods that could ultimately be scaled nationally and internationally, with applicability to other animal industries for which circular management is an aspiration for sustainability outcomes.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.086
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.003
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 head0.126
GPT teacher head0.292
Teacher spread0.166 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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