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Record W2415769750 · doi:10.21273/hortsci.35.3.418a

162 The Influence of Root Restriction on Flowering, Fruiting, Tree Growth, Yields, and Fruit Quality of Apple Trees

2000· article· en· W2415769750 on OpenAlexaff
Ross E. Byers, D.H. Carbaugh, L.D. Combs

Bibliographic record

VenueHortScience · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Physiology and Cultivation Studies
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsHorticultureGirdlingBiologyShootBotanyRootstockDwarfingApple treePlastic bagMaterials scienceComposite material

Abstract

fetched live from OpenAlex

`Fuji'/MM.111, `Pink Lady'/M.7A, and `Summerfield'/M.7A apple trees were planted in several types of individual root restrictive bags in the field in 1995. Bags were made of Knit and Woven fabrics, Galvanize hardware cloth (6.4 cm) with various holes sizes and of different bag volumes. The bags confined the development of large roots to within the bag. Roots that penetrated the bag resulted in root branching and large root inhibition. As the roots enlarged, roots penetrating the bags were restricted in diameter by the fabric hole size. Roots enlarged to some degree on both sides of the fabric holes but were not killed by girdling within the first few years. Root restriction bags decreased trunk caliper, shoot growth, pruning weights, number of cuts per tree, increased flowering, fruit numbers, and weight per tree. Fruit firmness, soluble solids and color was increased and starch was lower than the nonbagged controls. In cage and tank trials pine and/or meadow voles easily penetrated all of the fabric and polypropylene bags within 24 h, except for the galvanized hardware cloth (6.4 cm). Susceptibility of each material to vole damage was tested by placement of an apple inside a small bag of each. Root restriction bags seemed to be a viable alternative to dwarfing rootstocks for control of tree size, early flowering, and early fruiting.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.184

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 head0.026
GPT teacher head0.244
Teacher spread0.219 · 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

Citations1
Published2000
Admission routes1
Has abstractyes

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