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Record W3155246589 · doi:10.3390/plants10040742

Gas Exchange, Water Use Efficiency, and Biomass Partitioning among Geographic Sources of Acer saccharum Subsp. saccharum and Subsp. nigrum Seedlings in Response to Water Stress

2021· article· en· W3155246589 on OpenAlexaboutno aff
Richard J. Hauer, Hongxu Wei, Andrew K. Koeser, Jeffrey O. Dawson

Bibliographic record

VenuePlants · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsnot available
Fundersnot available
KeywordsSugarSaccharumBiomass partitioningMapleWater-use efficiencyEnvironmental scienceSeedlingAceraceaeStomatal conductanceEcotypeAgronomyHorticultureBiologyBotanyBiomass (ecology)Photosynthesis

Abstract

fetched live from OpenAlex

F. Michx.) source from Iowa. Seedling sources were selected for differences in temperature and precipitation of their geographic origins. Seedlings were preconditioned through moist (watered daily) or dry (watered every 4-7 days) cycles and then exposed to prolonged water stress. As water stress increased, dry preconditioned 17-week-old sugar maple seedlings from Oklahoma, Missouri, and Tennessee, sources from warmer, and/or drier climates with greater restrained photosynthesis, stomatal conductance, and water use efficiency than those from cooler and moister climates. Under imposed water stress, the Ontario and Iowa sourced seedlings increased their root to shoot ratios and decreased their specific leaf area, mechanisms for drought avoidance. However, no corresponding changes in these values occurred for Oklahoma, Missouri, and Tennessee sources and for the variable of leaf wilting across all sources. Results from this study suggest greater tolerance of water stress in the Oklahoma, Missouri, and Tennessee ecotypes from the western and southern range of sugar maple resulted primarily with water use efficiency (WUE) rather than other water stress coping mechanisms. Findings from this study provide evidence to support selection of sugar maples sources for forestation.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.594

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 head0.013
GPT teacher head0.207
Teacher spread0.195 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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