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Record W2021970525 · doi:10.1300/j301v03n01_12

Efficient Mowing for Pruning Wild Blueberry Fields

2004· article· en· W2021970525 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSmall Fruits Review · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBerry genetics and cultivation research
Canadian institutionsNova Scotia Department of Agriculture
Fundersnot available
KeywordsPruningEnvironmental scienceCropAgronomySpring (device)HorticultureVacciniumField experimentMathematicsBiologyAgroforestryEngineering

Abstract

fetched live from OpenAlex

SUMMARY Commercial wild blueberry (Vaccinium angustifolium Ait.) fields were mowed in spring and autumn at low (2.5-5 cm; 1-2 inches), medium (5-7.5 cm; 2-3 inches), and high (> 7.5 cm; > 3 inches) heights with a flail mower and also with a rotary mower (> 7.5 cm; > 3 inches), in order to determine optimal heights for mowing. Initial stem lengths reflected differences in mowing heights at both sites, but there were no differences in plant heights at the end of the pruning year growth, or in the spring of the crop year. There were no differences in buds per stem or in fresh fruit yields among the treatments at the Adams field, or among the flail mowed plots at the Murray Siding field. Yields in rotary mowed plots were lower than yields in all other plots at the Murray Siding field, and also stems were more branched than were stems in the other treatment plots. These results suggest that producers can mow their fields at higher heights without impact on plant growth and production, as long as they use the flail mower. Mowing at greater heights results in less damage to equipment, plants and soil, and is more economical than the low heights of mowing presently recommended for the industry.

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.

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 score0.965
Threshold uncertainty score0.243

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 head0.075
GPT teacher head0.289
Teacher spread0.215 · 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