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Record W2504572525 · doi:10.21273/hortsci.35.4.608

Is Fall Burning Preferable to Spring Burning for Promoting Growth Characteristics Favorable for Mechanical Harvesting in Vaccinium myrtilloides Michaux?

2000· article· en· W2504572525 on OpenAlexaff
S. P. Vander Kloet, Jason Pither

Bibliographic record

VenueHortScience · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBerry genetics and cultivation research
Canadian institutionsAcadia UniversityQueen's University
Fundersnot available
KeywordsVacciniumSpring (device)Prescribed burnHorticultureEricaceaeYield (engineering)WeedEnvironmental scienceBiologyBotanyAgronomyEcologyEngineeringMaterials science

Abstract

fetched live from OpenAlex

Periodic prescribed burns of lowbush blueberry barrens promote high yield, aid in weed control, and reduce fungal and insect damage. Whether such prescribed fires should be set in the autumn or the spring has been a matter of some dispute. Previous research on Vaccinium angustifolium Aiton suggested some advantages to autumnal burning, but few data have been collected on V. myrtilloides Michaux. To evaluate whether time of burning affected plant qualities most favorable for mechanical harvesting, such as stem length and lateral branching, a series of experiments was conducted on V. myrtilloides . Differences in stem length, numbers of lateral branches, and buds per stem were nonsignificant among plants burned in fall vs. those burned in spring. In three of four experiments, however, fall burns resulted in the growth of fewer lateral branches. Furthermore, among the four experiments, growth responses were more uniform following fall than following spring burns. We therefore suggest that, where possible, fall burns should be prescribed for blueberry plants that will be mechanically harvested.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.043
GPT teacher head0.274
Teacher spread0.232 · 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

Citations7
Published2000
Admission routes1
Has abstractyes

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