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Record W1567448050 · doi:10.1002/9780470958636.ch9

Cultural Practices: Focus on Major Barley‐Producing Regions

2010· other· en· W1567448050 on OpenAlex
J. R. Garstang, John Spink, Mekhlis Suleimenov, William F. Schillinger, Ross H. McKenzie, D. L. Tanaka, Salvatore Ceccarelli, Stefania Grando, Blakely Paynter, N. A. Fettell

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsAgronomyTillageWeed controlSowingHordeum vulgareCultural practiceSeedingCroppingAgroforestryAgricultureCrop rotationFertilizerCropping systemPloughEnvironmental scienceBiologyCropPoaceaeEcology

Abstract

fetched live from OpenAlex

This chapter contains sections titled: European Production and Yield Trends Crop Management Technical Expertise and Investment Bibliography Barley Types Planted and Ecoregional Distribution Barley in Crop Rotations Seeding Dates Seeding Rate Seeding Depth Tillage Practices Weeds and Weed Control Soil Fertility Management Complex of Cultural Practices Summary Introduction Climate in Relation to Barley Production Cropping Systems Cultural Practices Fertilizer Management Weeds, Diseases, and Insects Future Outlook Fertilizer Use Rotations Tillage Sowing Seeding Rates Planting Dates Weed Control Acknowledgments Cropping Environment for Barley in Australia Barley Production - Area and Tons Types of Barley Grown Place of Barley in the Farming System Soil Types on Which Barley is Grown Date of Seeding Plant Population and Seed Rate Nutrition with a Focus on Nitrogen Pest Management References

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.030
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0150.002

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 head0.045
GPT teacher head0.275
Teacher spread0.230 · 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