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Record W3002816171 · doi:10.1139/cjfr-2019-0398

Adjusting for the effect of missing or dominated plants in progeny and clonal trials of <i>Eucalyptus</i>

2020· article· en· W3002816171 on OpenAlexvenueno aff
Getulio Caixeta Ferreira, Aurélio Mendes Aguiar, Bruno Marco de Lima, José Luís Lima, Gabriel Dehon Sampaio Peçanha Rezende, Magno Antônio Patto Ramalho

Bibliographic record

VenueCanadian Journal of Forest Research · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetics and Plant Breeding
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsBiologyDiameter at breast heightMissing dataEucalyptusTraitSelection (genetic algorithm)Clonal selectionHorticultureBotanyStatisticsMathematicsComputer science

Abstract

fetched live from OpenAlex

The objective of this paper was to estimate the effect of either missing or dominated plants (those that developed poorly) in experiments evaluating progenies or clones of Eucalyptus. Additionally, it was to investigate whether the use of the area available per plant is a suitable strategy to mitigate the effect of missing plants. Lastly, it was to evaluate whether missing or dominated plants in the experiments affected the association between plant performance in a progeny trial (PT) and their respective clones in a clonal trial (CT). Five 5-year-old PTs and four 3-year-old CTs were used. The recorded trait was diameter at breast height (DBH). The area available per plant was used to carry out the adjustment, taking into consideration the absence of neighboring plants as well as dominated plants. It was found that with the level of missing plants below 20% in experiments, the adjustment using the area available per plant did not improve the efficiency of the selection of either PTs or CTs. The strategy of considering not only missing plants but also dominated plants is not beneficial for the adjustment.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
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 head0.211
GPT teacher head0.344
Teacher spread0.132 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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