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Record W2397496826 · doi:10.1139/cjss-2015-0111

Impact of potassium sulfate salinity on growth and development of cranberry plants subjected to overhead and subirrigation<sup>1</sup>

2016· article· en· W2397496826 on OpenAlexafffundvenue
Michael Samson, J. André Fortin, Steeve Pépin, Jean Caron

Bibliographic record

VenueCanadian Journal of Soil Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBerry genetics and cultivation research
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIrrigationSalinityGreenhouseEnvironmental scienceSoil salinityYield (engineering)Field experimentAgronomyHorticultureSoil waterSoil scienceBiologyPhysics

Abstract

fetched live from OpenAlex

New recommendations in cranberry production suggest reducing overhead irrigation and the use of subirrigation as an alternative irrigation method, two strategies suspected to increase the risk of salt buildup in soil. Because very little is known about cranberry tolerance to salinity, this study was conducted to determine if deficit irrigation and subirrigation could cause salinity issues and affect plant yield. In a greenhouse, cranberry plants were submitted to eight different treatments combination from two irrigation methods (overhead irrigation and subirrigation) and four salinity levels created by increasing amounts of applied K2SO4 (125 (control), 2500, 5000, and 7500 kg K2O ha−1). Irrigation methods showed no significant difference in measured electrical conductivity of soil solution (ECss). Meanwhile, growth and yield parameters decreased significantly with soil salinity in both irrigation treatments, and an average ECss of 3.2 dS m−1 during flowering caused a 22% drop in relative photosynthetic rate and a 56% decrease in yield when compared with the control. Cranberry seems to be salt sensitive, and further work should investigate ECss levels under different field and irrigation practices to make sure that it does not reach critical levels.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.034
GPT teacher head0.261
Teacher spread0.227 · 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 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

Citations10
Published2016
Admission routes3
Has abstractyes

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