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Record W4389047795 · doi:10.1201/b22954-2

Soil Water Conservation for Dryland Farming

2023· book-chapter· en· W4389047795 on OpenAlexaboutno aff
Paul W. Unger, Robert C. Schwartz, R. Louis Baumhardt, Qingwu Xue

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsDryland farmingSoil conservationAgroforestryAgricultureConservation agricultureEnvironmental scienceWater conservationDryland salinityGeographyAgronomySoil biodiversitySoil scienceSoil waterSoil fertilityBiologyIrrigationArchaeology

Abstract

fetched live from OpenAlex

In North America, dryland farming is highly important in the Canadian Prairies and in the United States Great Plains, the US Pacific Northwest, the US Southwest, and parts of the US Intermountain Areas. Because the amount and seasonal distribution of precipitation in semiarid regions is highly variable from year to year, soil water conservation is critical for maximizing the available stored soil water at planting so that crops can produce at their potential. Successful water management under dryland farming conditions invariably entails conserving soil water during fallow periods for subsequent use by dryland crops and employing management interventions during the growing season that have the potential to improve crop productivity. However, cover crops also extract greater quantities of soil water and at deeper soil depths compared with soil water evaporation under fallow and thereby can reduce available water at planting and subsequent crop yields.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.025
Threshold uncertainty score0.084

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0250.004

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 head0.065
GPT teacher head0.229
Teacher spread0.164 · 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 designNot applicable
Domainnot available
GenreOther

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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