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Record W4293237120

Water activity measurement on seed or pollen lots

2008· preprint· en· W4293237120 on OpenAlexaffabout
Patrick Baldet, Fabienne Colas, M. Bettez

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2008
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicGreenhouse Technology and Climate Control
Canadian institutionsMinistère des Ressources naturelles et des Forêts (Québec)
Fundersnot available
KeywordsPollenComputer scienceBotanyBiology
DOInot available

Abstract

fetched live from OpenAlex

A presentation of the measurement of the activity of water was carried out among a group of experts and scientists of the Tree Seed Working Group of the Canadian Tree Improvement Association at the time of the visit of the installations of the Seed Centre of Berthier within the framework of the pre-turn of joint congress IUFRO-ACAA/CTIA 2008 in Quebec. During a demonstration we presented the theoretical and operational aspects of the measurement of the activity of water in the laboratory of the only centre of forest seeds of Quebec which recently adopted this method for seeds and pollens management. The simplicity and the speed of acquisition of measurement as well as the non-destructive character of the procedure were underlined in particular. / Une présentation de la mesure de l'activité de l'eau a été réalisée auprès d'un groupe de praticiens et de scientifiques du Tree Seed Working Group de l'Association Canadienne d'Améliorateurs forestiers lors de la visite des installations du centre de semences de Berthier dans le cadre du pré-tour du congrès IUFRO-ACAA 2008 à Québec. Ont été présentés les aspects théoriques et opérationnels de la mesure de l'activité de l'eau lors d'une démonstration dans le laboratoire du seul centre de semences forestières du Québec qui a récemment adopté cette méthode de contrôle des semnces et pollens. La simplicité et la rapidité d'acquisition de la mesure ainsi que le caractère non destructif de la procédure ont été soulignés en particulier.

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)0.003
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 score0.391
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.035
GPT teacher head0.215
Teacher spread0.180 · 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

Citations0
Published2008
Admission routes2
Has abstractyes

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