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Record W2362508211

Investigation on the Growth and Water Consumption Characteristics of the Seedlings of Some Populus tomentosa Hybrid Clones

2010· article· en· W2362508211 on OpenAlexaboutno aff
Fang Xiao-juan

Bibliographic record

VenueXibei Linxueyuan xuebao · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsnot available
Fundersnot available
KeywordsWater consumptionHorticultureAnimal scienceBiologyBiomass (ecology)BotanyAgronomyEnvironmental scienceEnvironmental engineering
DOInot available

Abstract

fetched live from OpenAlex

The growth and water consumption characteristics of the seedlings of nine 1(2)-0 type Populus tomentosa clones were studied by BP3400 precision balance and Canadian Epson Twain Pro scanner.Other indices such as height,diameter,water consumption features,rates of leaf water losing,specific leaf weight,biomass etc.were compared.The results showed that: clones 30,BL5,S86 were as superior clones to cultivate P.tomentosa forests under the condition of supply water.They groped rapidly and their heights were 110.17 cm,149.30 cm and 161.50 cm respectively,and their diameters were 1.14 cm,1.24 cm and 1.12 cm respectively.Their SLW of leaves and biomasses were larger than other clones.SLW of leaves were 103.39,99.44,95.80 g﹒m-2 respectively in October.Biomasses were 66.71 g,62.96 g,59.739 g respectively.Daily variations of water consumption and water consumption rate were the single type.The peak occurred at 12:00 ~ 14:00.The rates of water consumption in daytime of clones S86,83,42 were lower,those were 198.81 g·m-2·h-1,241.44 g·m-2·h-1,87.93 g·m-2·h-1 in August respectively,and their rates of leaf water losing presented lower,the rates were 55.74%,48.71%,41.88% after the leaves isolated 8 hours in August respectively.So clones S86,83,42 were as superior clones to cultivate P.tomentosa forests under the condition of limited water.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.118

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 head0.024
GPT teacher head0.192
Teacher spread0.168 · 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 designBench or experimental
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

Citations0
Published2010
Admission routes1
Has abstractyes

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