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Record W2016558895 · doi:10.1139/x02-157

Promotion of flowering in<i>Eucalyptus nitens</i>by paclobutrazol was enhanced by nitrogen fertilizer

2003· article· en· W2016558895 on OpenAlexvenueno aff
Dean Williams, BM Potts, P. J. Smethurst

Bibliographic record

VenueCanadian Journal of Forest Research · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Physiology and Cultivation Studies
Canadian institutionsnot available
FundersNorske SkogCommonwealth Scientific and Industrial Research Organisation
KeywordsPaclobutrazolNitrogenEucalyptus nitensBiologyPhosphorusAgronomyFertilizerFactorial experimentEucalyptusBotanyHorticultureChemistryMathematics

Abstract

fetched live from OpenAlex

We examined the effects of nitrogen and phosphorus fertilizer and paclobutrazol on flowering precocity and abundance in Eucalyptus nitens Deane &amp; Maid. Trials to test these effects consisted of a factorial nitrogen by phosphorus experiment replicated on two sites and factorial nitrogen by paclobutrazol experiments conducted separately on reproductively immature and reproductively mature trees. The increase in tree size due to nitrogen fertilization increased the occurrence and abundance of precocious flower buds. However, the increase in tree size alone could not account for the nitrogen effect on flowering, indicating a secondary mechanism of flower induction by nitrogen. The application of both nitrogen fertilizer and paclobutrazol substantially increased the occurrence of precociously flowering trees over that of either treatment applied alone. The efficacy of both paclobutrazol and nitrogen in stimulating the flowering of the reproductively mature trees was affected by soil type, but this effect was overcome through the combination of nitrogen and paclobutrazol. The combination of nitrogen and paclobutrazol also restricted tree growth and combined applications on nitrogen deficient sites will be beneficial in commercial systems for producing eucalypt seed.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.975

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 head0.047
GPT teacher head0.278
Teacher spread0.231 · 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

Citations41
Published2003
Admission routes1
Has abstractyes

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