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Record W2171266434 · doi:10.14430/arctic617

Climatic Impact on Small Grain Production in the Subarctic Region of the United States

2003· article· en· W2171266434 on OpenAlex
Brenton Sharratt, Charles W. Knight, F. J. Wooding

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueARCTIC · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsSubarctic climatePrecipitationGrowing seasonAgronomyEnvironmental scienceHordeum vulgareYield (engineering)GeographyPoaceaeBiologyEcology

Abstract

fetched live from OpenAlex

The Subarctic comprises the higher mid-latitudinal regions with short, cool, moist summers and long, cold, dry winters. Indeed, the short, cool growing season is often thought of as a barrier to crop growth and diversity in these regions. Little is known, however, concerning the impact of the Subarctic climate on crop production. This study aimed to identify the climatic factors that are most important to the production of small grains in the Subarctic region of Alaska. The impact of climate on 'Galt' and 'Weal' barley (Hordeum vulgare L.), 'Nip' and 'Toral' oat (Avena sativa L.), and 'Gasser' and 'Park' wheat (Triticum aestivum L.) was assessed using climate and grain yield data collected from 1972 to 1989 at Fairbanks. Multiple regression analysis was used to identify the climatic factors that most influence yield. Different factors accounted for the largest proportion of variability in yield across years for the different grains. 1) For barley, variations in precipitation deficit (pan evaporation minus precipitation) and distribution of precipitation events within a growing season accounted for 41% of the variability across years in yield of Galt and Weal cultivars. 2) For oat, variations in the precipitation deficit ratio (ratio between precipitation deficit and pan evaporation) accounted for 44% of the variability across years in yield of Nip and 58% in yield of Toral oat. 3) For wheat, variations in number of days between precipitation events within a growing season, precipitation deficit, and temperature explained 70% of the variability across years in yield of Gasser and Park wheat. Results from our analysis further indicated that small grain production was bolstered in seasons with greater precipitation, more frequent precipitation, or lower evaporative demand (pan evaporation). Only wheat production appeared to be favored by higher minimum air temperatures. This study suggests that, despite the cool growing season in interior Alaska, the primary climatic limitation to crop production is water stress, associated with low precipitation or high evaporative demand. Therefore, land management practices aimed at conserving soil water will likely bolster crop production in the Subarctic.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.172

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 science0.0000.000
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 head0.044
GPT teacher head0.245
Teacher spread0.200 · 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