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Record W4246549259 · doi:10.1002/9781405181099.k0907

Douglas Fir

2008· other· en· W4246549259 on OpenAlexaff
Santosh Misra, Mariana Vetrici, John N. Owens

Bibliographic record

Venuenot available
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Reproductive Biology
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsBiologySomatic embryogenesisAgrobacteriumBotanyPollinationAdaptabilityTransgeneCell biologyGeneEcologyPollenGeneticsEmbryoEmbryogenesis

Abstract

fetched live from OpenAlex

Abstract Douglas fir is a native species of western North America, China, Taiwan, and Japan. Its high quality lumber is in demand throughout the world and therefore, breeding objectives include improvement of economic value and adaptability to biotic and abiotic stresses. Similar to other conifers, the reproductive cycle of Douglas fir is lengthy (17 months) and the time from pollination to cone maturity is 6 months. The pollination mechanism is unique in that pollen granule takes 9 weeks to germinate and elongate. The most efficient method for improving economic value is by capturing multiple desirable traits in a single individual and propagation of this genotype via somatic embryogenesis. Presently, the main incentives for transgenic breeding are to provide methods to confer insect and fungal resistance, and the temporary transfer of genes that may induce somatic embryogenesis. Thus far, experiments with Agrobacterium ‐mediated transformation have identified several strains that effectively transform Douglas‐fir tissues, and stem woodiness, plant age, and plant defense mechanisms have been identified as factors that influence success rate. Electroporation and microprojectile bombardment have also been investigated and cytokinin pulsing was shown to improve transformation efficiency. The use of endogenous and/or tissue‐specific promoters will be useful in enhancing and ontrolling transgene expression.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.034
Threshold uncertainty score1.000

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)0.0010.000

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 head0.007
GPT teacher head0.221
Teacher spread0.213 · 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 designNot applicable
Domainnot available
GenreOther

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 routes1
Has abstractyes

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