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Record W1963740469 · doi:10.4141/p02-044

The impact of lime and organic fertilization on the growth of wild-simulated American ginseng

2003· article· en· W1963740469 on OpenAlexafffundvenue
I. Nadeau, R. R. Simard, Alain Patrick Olivier

Bibliographic record

VenueCanadian Journal of Plant Science · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGinseng Biological Effects and Applications
Canadian institutionsAgriculture and Agri-Food CanadaUniversité Laval
FundersAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsLimeGinsengFertilizerHuman fertilizationMapleSugarHorticultureShootSoil pHOrganic fertilizerSoil acidificationEnvironmental scienceAgronomyBiologyBotanyChemistrySoil waterEcologyFood scienceMedicine

Abstract

fetched live from OpenAlex

A 5-yr experiment was undertaken in a red maple forest to evaluate the effects of lime and organic fertilizer application on the growth parameters of wild-simulated American ginseng growing on a very acidi c soil. The application of lime had a positive impact on ginseng emergence and survival rate; it also significantly increased soil Ca, as compared to no application (control). During the last years of the experiment, adding lime also increased leaf area, a s well as shoot and root mass of ginseng. Adding lime and organic fertilizer together positively affected ginseng survival rate and root mass, as compared to adding lime alone. These results indicate that liming can improve the growth and survival of Amer ican ginseng during the first 5 yr of its development in this very acidic maple forest soil. The improvement could be due, at least partly, to increased Ca content in the soil. Addition of organic fertilizer would be beneficial as long as sufficient Ca2+is provided to alleviate the Al toxicity of this soil. Thus, using such cultural practices, wild-cultivation of American ginseng, even in red maple forests, could constitute a valuable alternative to field-cultivation. Key words:

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)0.001
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 score0.385
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.009
GPT teacher head0.235
Teacher spread0.226 · 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

Citations19
Published2003
Admission routes3
Has abstractyes

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