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Record W2288586133

Evaluation préliminaire de l' enneigement artificiel comme méthode de protection hivernale de la vigne au Québec

2000· article· fr· W2288586133 on OpenAlexaboutno aff
Jean Dubois, Y Jolivet

Bibliographic record

VenueInternational journal of vine and wine sciences · 2000
Typearticle
Languagefr
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsnot available
Fundersnot available
KeywordsForestryGeography
DOInot available

Abstract

fetched live from OpenAlex

Des mesures de temperatures ont ete enregistrees sur quatre parcelles experimentales durant la saison froide dans un vignoble au Quebec. Une parcelle enneigee artificiellement a des fins de protection contre les froids hivernaux a ete comparee a trois autres, dont une parcelle de neige naturelle, une parcelle enfeuillee avec neige naturelle et une autre parcelle de neige naturelle avec de fines croutes de glace a la suite des chutes de neige. Les resultats montrent que, pendant les grands froids de janvier et de fevrier, les sarments de la vigne situes a 30 cm du sol et proteges par la neige artificielle ont conserve des temperatures minimales negatives beaucoup plus elevees, et ce avec une difference pouvant atteindre 23°C, comparativement aux sarments situes a une meme hauteur sur les autres parcelles. Les resultats revelent aussi qu?une couche de neige de 15 a 20 cm suffit a isoler totalement les sarments de l?air ambiant. L?utilisation de l?enneigement artificiel comme methode de protection contre le froid s'avere donc efficace. Cependant, lors de l'enneigement artificiel a l'automne, lorsque le couvert nival est encore absent, l'eau non cristallisee penetre le sol jusqu?a la zone racinaire par percolation et en abaisse la temperature de l'ordre de 3°C jusqu'a 60 cm de profondeur. De meme, par rapport aux moyens traditionnels de protection, au printemps, la fonte hâtive du couvert nival au centre des rangs laisse les sarments de la vigne sans protection et les expose aux gels tardifs.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.875
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.117
GPT teacher head0.374
Teacher spread0.256 · 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.

Study designOther design
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

Citations1
Published2000
Admission routes1
Has abstractyes

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