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Record W7116777810 · doi:10.71318/apom.2020.74.4.210

Effects of Growing Location on Fruit Tree RootSuckers

2020· article· W7116777810 on OpenAlexaboutno aff
Richard P. Marini

Bibliographic record

VenueJournal of American Pomological Society · 2020
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicPlant Physiology and Cultivation Studies
Canadian institutionsnot available
FundersNational Institute of Food and AgriculturePennsylvania Department of AgricultureU.S. Department of Agriculture
KeywordsSuckerRootstockOrchardShootTraitSeed orchardRoot systemVegetative reproduction

Abstract

fetched live from OpenAlex

Root suckers are vegetative shoots that develop from root buds and are generally considered a negative trait of a rootstock. Root sucker production for a given rootstock often varies with site. Published data from three peach and five apple multi-location NC-140 rootstock trials were used to determine if some sites are more prone to root sucker development than other sites. For each trial, the number of root suckers per site were ranked in ascending order and the correlations of the ranks for pairwise combinations of trials were evaluated with Spearman’s rank correlation coefficient. Although the associations were usually not significant at the 5% level, certain sites consistently had higher rankings. Sites with high rankings for peach root suckers also tended to have high rankings for apple root suckers. Sites with consistently high rankings included Utah, Pennsylvania, Kentucky and South Carolina. British Columbia and Ontario had consistently low rankings. Sites with high rankings in some trials and low rankings in other trials included North Carolina, New York, and Maryland. Although root sucker production is ultimately controlled by genetics, these analyses suggest that factors related to the site, such as soil conditions, environmental factors or orchard practices also influence the development of root suckers.

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)0.001
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 score0.935
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.235
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

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

Citations0
Published2020
Admission routes1
Has abstractyes

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