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Record W6959636340 · doi:10.1139/cjps-2014-365

AAC Sylvia-Arlene rose for rosehip production

2015· article· en· W6959636340 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Practices and Plant Genetics
Canadian institutionsnot available
Fundersnot available
KeywordsRose (mathematics)Interspecific competitionYield (engineering)Positive controlLine drawings

Abstract

fetched live from OpenAlex

Fofana, B. and Sanderson, K. 2015. AAC Sylvia-Arlene rose for rosehip production. Can. J. Plant Sci. 95: 609-613. AAC Sylvia-Arlene is a semi-domesticated rose variety demonstrating quality and yield to meet the criteria for economic production. AAC Sylvia-Arlene was evaluated in Charlottetown between 2005 and 2012, and was found to be adapted in field and different agronomic practices. In comparison to the control lines S36, S68, S140, and S142, the line S26 was characterized as a natural interspecific hybrid of Rosa carolina×Rosa virginiana and was denominated as ‘AAC Sylvia-Arlene’ whereas S36 and S68 are Rosa virginiana species. AAC Sylvia-Arlene yielded more rosehips than all the control lines. The plants of AAC Sylvia-Arlene are taller and wider than those of the reference varieties. The flowers of AAC Sylvia-Arlene are light-pink, whereas those of S36, S140 and S142 are medium-pink. The fruits of AAC Sylvia-Arlene are longer and wider than those of the reference varieties. AAC Sylvia-Arlene's fruit contains more seed than that of S36 and S140, was similar to S142 and contains less seed compared with S68. The seed weight per fruit of AAC Sylvia-Arlene and S142 is lower than that of other references. The antioxidant capacity of AAC Sylvia-Arlene fruit was similar to that of S36 and higher than any other control lines. AAC Sylvia-Arlene showed a unique terpenoid content, higher than that of any other control lines.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.875
Threshold uncertainty score0.458

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 head0.546
GPT teacher head0.258
Teacher spread0.288 · 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 designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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