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Record W3032626198 · doi:10.3390/plants9060682

Effects of pH and Mineral Nutrition on Growth and Physiological Responses of Trembling Aspen (Populus tremuloides), Jack Pine (Pinus banksiana), and White Spruce (Picea glauca) Seedlings in Sand Culture

2020· article· en· W3032626198 on OpenAlexafffund
Xu Feng, Maryamsadat Vaziriyeganeh, Janusz J. Zwiazek

Bibliographic record

VenuePlants · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPinus <genus>Jack pineBotanyWoody plantWhite (mutation)SalicaceaeBiology

Abstract

fetched live from OpenAlex

) seedlings to root zone pH ranging from 5 to 9 were studied in sand culture in the presence of two mineral nutrition levels. After eight weeks of treatments, effects of pH on plant dry weights varied between the plant species and were relatively minor in white spruce. Higher nutrient supply significantly increased dry weights only in trembling aspen subjected to pH 5 treatment. There was little effect of pH and nutrition level on net photosynthesis and transpiration rates in white spruce and jack pine, but net photosynthesis markedly declined in aspen at high pH. Chlorophyll concentrations in young foliage decreased the most in trembling aspen and jack pine. The effects of high pH treatments on the concentrations of Mg, P, Ca, Mn, Zn, and Fe in young foliage varied between the plant species with no significant decreases of Fe and Zn recorded in trembling aspen and white spruce, respectively. This was in contrast to earlier reports from the studies carried out in hydroponic culture. The sand culture system that we developed could be a more suitable alternative to hydroponics to study plant responses to pH in the root zone. Plant responses to high pH appear to involve complex events with a likely contribution of nutritional effects and altered water transport processes.

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)0.000
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 score0.459
Threshold uncertainty score0.509

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.020
GPT teacher head0.229
Teacher spread0.209 · 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

Citations17
Published2020
Admission routes2
Has abstractyes

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