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Record W2166365572

Dry matter partitioning and physiological responses of Coffea arabica varieties to soil moisture deficit stress at the seedling stage in Southwest Ethiopia

2010· article· en· W2166365572 on OpenAlexaff
Mohammed Worku, Tess Astatkie, Nova Scotia

Bibliographic record

VenueAfrican Journal of Agricultural Research · 2010
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSeedlingShootDry weightHorticultureWater stressWater contentBiomass (ecology)BiologyDry matterAgronomy
DOInot available

Abstract

fetched live from OpenAlex

varieties were tested in 15 and 30 days water stress followed by 15 days re-watering at seedling stage in Jimma, southwest Ethiopia. Repeated measures analysis revealed that differences among varieties depended on water stress and recovery periods for leaf P content and shoot mass ratio (SMR). Regardless of stress and recovery periods, significant differences among varieties were found for root fresh weight (RFW), leaf dry weight (LDW), leaf mass ratio (LMR), root mass ratio (RMR) and root to shoot ratio (RSR). Varieties 7440, 7487, 74140 and 74148 showed relatively high biomass allocation to roots whereas variety 741 allocated more to shoots. Variety 7487 had higher RFW and LDW. Significant differences among stress and recovery periods were also obtained; leaf K, Ca and Mg contents and SMR significantly increased whereas leaf P content, LMR, RMR and RSR decreased during stress. Higher leaf and root biomass fraction, and fresh weights were obtained after 15-day stress and recovery, respectively but similar root biomass fraction after 30-day stress and 15-day recovery and fresh weights during stress. Significantly higher leaf folding, stomatal resistance, leaf temperature and wilted seedlings and the lowest relative water content were observed after 30-day water deficit. Overall, variable coffee plant responses to drought stress periods, and faster recovery of the seedlings after re-watering were observed. Key words:

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)0.002
metaresearch head score (Gemma)0.003
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 score0.578
Threshold uncertainty score0.833

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
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 integrity0.0000.002
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 head0.065
GPT teacher head0.355
Teacher spread0.289 · 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

Citations13
Published2010
Admission routes1
Has abstractyes

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