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Record W2528408649 · doi:10.1139/cjfr-2016-0202

Physiological responses to alternative flooding and drought stress episodes in two willow (<i>Salix</i> spp.) clones

2016· article· en· W2528408649 on OpenAlexvenueno aff
Guillermo Doffo, Silvia Monteoliva, María Emilia Rodríguez, Virginia Luquez

Bibliographic record

VenueCanadian Journal of Forest Research · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to water stress
Canadian institutionsnot available
FundersConsejo Nacional de Investigaciones Científicas y Técnicas
KeywordsWillowFlooding (psychology)SalicaceaeBiologyDrought stressGrowing seasonclone (Java method)Stomatal conductanceShootBotanyHorticultureAgronomyWoody plantPhotosynthesis

Abstract

fetched live from OpenAlex

Climate change will increase the occurrence of flash floods as a consequence of extreme rain events, creating alternate periods of drought and flooding during the growing season. We analyzed the responses of two willow clones with contrasting responses to flooding (clone B, Salix matsudana × Salix alba hybrid; clone Y, Salix alba) to different combinations of stress treatments: continuous flooding or drought for six weeks, or cyclic treatments of two weeks of stress separated by two weeks of watering at field capacity. Drought reduced growth, stomatal conductance, and total leaf area in both clones, but flooding did not. Flooding reduced the root to shoot ratio in both clones. The hydraulic conductivity of the main stem was significantly reduced by drought only in clone Y. The area of the vessels was decreased by both drought and flooding, but the number was increased only by drought. The occurrence of drought before flooding reduced the vessel area, but the opposite treatment did not. An episode of drought after one of flooding is more stressful than the opposite situation, especially for clone Y, which could not adjust its water transport capacity during the drought period.

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)0.001
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 score0.572
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.073
GPT teacher head0.325
Teacher spread0.252 · 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

Citations36
Published2016
Admission routes1
Has abstractyes

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