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Record W2135819683 · doi:10.5539/jas.v4n6p90

Evaluating Alfalfa (Medicago sativa L.) Cultivars for Salt Tolerance Using Laboratory, Greenhouse and Field Methods

2012· article· en· W2135819683 on OpenAlexvenueno aff
John Derek Scasta, Calvin Trostle, Mike Foster

Bibliographic record

VenueJournal of Agricultural Science · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Stress Responses and Tolerance
Canadian institutionsnot available
FundersTexas Department of AgricultureTexas Tech University
KeywordsMedicago sativaCultivarGreenhouseSalinityAgronomySoil salinityForageIrrigationGerminationEnvironmental scienceBiologyHorticultureEcology

Abstract

fetched live from OpenAlex

Salinity is a limiting factor in irrigated alfalfa (Medicago sativa L.) production in many regions of the world. Objectives of this project were to evaluate twelve alfalfa cultivar responsesto salinity using laboratory, greenhouse and field experiments. The laboratory experiment evaluated plant germination under increasing levels of salt concentration (0 to 2%) and statistical differences were observed at 1.0%, 1.5% and 2.0% concentrations (10 000 ppm to 20 000 ppm). The greenhouse experiment evaluated forage production of cultivars using Salt Control Ratio (SCR) values using 3840 ppm irrigation water and significant differences were found between cultivars. The field experiment evaluated forage production under saline field conditions west of Pecos, TX (soil salinity 2131 ppm, water salinity 2950 ppm) with significant differences between cultivar yields.Canonical Correspondence Analysis (CCA), a constrained ordination technique,revealed statistical significance of canonical axes in explaining variation between alfalfa cultivars and laboratory, greenhouse and field experiments.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.068
GPT teacher head0.376
Teacher spread0.308 · 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

Citations35
Published2012
Admission routes1
Has abstractyes

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