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Record W2922249593 · doi:10.1139/cjps-2018-0159

Flowering and yield of eastern filbert blight resistant hazelnut cultivars in southwestern British Columbia

2019· article· en· W2922249593 on OpenAlexfundvenueaboutno aff
Thomas E. O’Dell

Bibliographic record

VenueCanadian Journal of Plant Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsnot available
FundersAgriculture and Agri-Food CanadaOregon State University
KeywordsCultivarBlightBiologyHorticultureYield (engineering)OrchardBotanyAgronomy

Abstract

fetched live from OpenAlex

Trees of six recently released hazelnut (Corylus avellana) cultivars (Eta, Gamma, Jefferson, Sacajawea, Theta, and Yamhill) were grown at five locations in southwestern British Columbia to evaluate female receptivity and pollen shed timing, disease resistance, and nut yield. The overlap of female receptivity and pollen shed of mating-compatible cultivars equaled or exceeded that reported elsewhere. Following 4–6 yr of field growth without fungicides at sites adjoining eastern filbert blight (EFB)-infected orchards, EFB symptoms were absent from four cultivars (Eta, Gamma, Theta, Yamhill), negligible on Jefferson, and substantial on Sacajawea. Two additional potential diseases, Phomopsis sp. and Phytophthora sp., were observed on several trees at multiple farms. Yields varied by cultivar and farm, averaging 2.9 kg per tree of sixth-leaf Jefferson and 1.0 kg per tree of fourth-leaf Yamhill; the highest yield per tree observed was 9.3 kg for fifth-leaf Jefferson. These data, the first describing flowering and yield of these cultivars from operational orchards and the only such data from British Columbia, demonstrate the potential for new hazelnut cultivars even where pressure from eastern filbert blight is high. Also revealed are two potential emergent diseases for which growers need to remain vigilant.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.691
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.019
GPT teacher head0.208
Teacher spread0.189 · 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 designObservational
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

Citations4
Published2019
Admission routes3
Has abstractyes

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