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Record W2380700036

Effects of supplemental lighting treatments on seedling growth of varied provenances in black spruce.

2010· article· en· W2380700036 on OpenAlexaboutno aff
Fangqun Ouyang, Shougong Zhang, Junhui Wang, Jianwei Ma, Jiang Ming, Wang Mei-qin, Yue Li

Bibliographic record

VenueBeijing Linye Daxue xuebao · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsnot available
Fundersnot available
KeywordsSeedlingBiologyBlack spruceHorticultureSunlightBotanyShootFluorescent lightPhytotronFluorescenceEcology
DOInot available

Abstract

fetched live from OpenAlex

To understand the seedling trait performance from different black spruce(Picea mariana) provenances with varied light treatments and to establish efficient propagation technique system with supplemental light,container seedlings of 10 black spruce provenances from Canada were treated by 3 light sources of fluorescent lamp,sunlight dysprosium lamp and tungsten lamp.The results showed that light treatment could inhibit seedling dormancy and promote sustaining growth.The seedling height by using fluorescent light for 8 hrs at midnight for 135 days could be up to 5.47 times compared with control.The effect of light sources was significant on seedling growth,shoot and root formation.The treatment for sunlight dysprosium lamps was best,and fluorescent lamp was more economical and convenient.Besides,the seedling grew well when fluorescent lamp was supplemented for 8 hrs at midnight.There were significant interaction effects on seedling traits between light sources and provenances.It was indicated that there were obvious differences for different black spruce provenances in adapting to light environment.Therefore,it should be paid more attention to select appropriate provenances for introduction experiment.The light source should be selected according to the seedling provenances to achieve the optimum breeding results.

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 score0.212
Threshold uncertainty score0.814

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 head0.007
GPT teacher head0.223
Teacher spread0.216 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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