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Record W1999144160 · doi:10.1080/01904160009382044

Carbon and nitrogen supplementation to soybean through stem injection and its effect on soybean plant senescence

2000· article· en· W1999144160 on OpenAlexaff
Xiaomin Zhou, O. A. Abdin, Bruce Coulman, D. C. Cloutier, M. A. Faris, Donald L. Smith

Bibliographic record

VenueJournal of Plant Nutrition · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoybean genetics and cultivation
Canadian institutionsAgriculture and Agri-Food CanadaMcGill University
Fundersnot available
KeywordsSenescenceNitrogenBiologyCarbon fibersPlant growthAgronomyBotanyHorticultureChemistryMathematics

Abstract

fetched live from OpenAlex

Abstract Plant senescence studies have indicated that internal competition for nutrients such as carbon (C) and nitrogen (N) can be an important factor in the initiation of senescence. A greenhouse experiment was conducted to determine the effect of increased supplies of C and N on senescence of soybean (Glycine max [L.] Merr) plants. Soybean plants were injected with solutions of sucrose (150gL‐1), N (15 mM N), and distilled water from the onset of flowering until senescence using a modified stem injection technique. The average uptake rate of all solutions was 1.3 mL d‐1 per plant. The plants injected with sucrose accumulated the most biomass, followed by those injected with N and distilled water. Soybean plants injected with sucrose senesced 17 days later than the distilled water control while senescence was not delayed for plants injected with N. Injection of either N or sucrose increased the concentration and content of N in soybean plants. The results indicated that intra‐plant‐competition for reduced C plays an important role in plant senescence. Because the total amount of N injected was only 2% of the total plant N, as compared to 31 % for C, the role of intra‐plant competition for N was less clear.

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 score0.140
Threshold uncertainty score0.196

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 head0.018
GPT teacher head0.227
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 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

Citations3
Published2000
Admission routes1
Has abstractyes

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