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Record W4384154332 · doi:10.1139/cjfr-2023-0038

Genetic control and early selection of three <i>Corymbia</i> species

2023· article· en· W4384154332 on OpenAlexvenueno aff
Letícia Miranda, Regiane Abjaud Estopa, João Gabriel Zanon Paludeto, Evandro Vagner Tambarussi

Bibliographic record

VenueCanadian Journal of Forest Research · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Taxonomy and Phylogenetics
Canadian institutionsnot available
Fundersnot available
KeywordsHeritabilitySelection (genetic algorithm)BiologyGenetic gainGenetic variationGenetic correlationVeterinary medicineHorticultureAnimal scienceGeneticsGeneMedicine

Abstract

fetched live from OpenAlex

The main objective of this study was to investigate genetic control for individual volume and genetic and phenotypic correlation between trait measured at two different ages. We also assessed three different selection intensities ( i = 1%, i = 5% and i = 10%) to understand the effects on genetic gain and effective size. Eight progeny tests were evaluated which included three tests of Corymbia citriodora subsp. citriodora (CCT), two tests of C. citriodora subsp. variegata (CCV), and three tests of C. torelliana (CTO). Narrow-sense heritability [Formula: see text] ranged from 0.26 to 0.62 for the CCT tests, from 0.07 to 0.21 for the CCV tests, and from 0.14 to 0.69 for CTO. The coefficients of individual genetic variation ([Formula: see text]) ranged from 22.5% to 63.9% for CCT, from 19.3% to 28.3% for CCV, and from 22.8% to 41.3% for CTO. Considering a selection intensity of 10%, the N e after selection would range from 31 to 98 for CCT, 36 to 47 for CCV, and 45 to 62 for CTO. For the TP8 CTO test, a selection intensity greater than 10% is recommended. With a selection intensity of 10%, genetic gains ranged from 25 to 107% for CCT, from 14 to 27% for CCV, and from 19 to 64% for CTO.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.065
GPT teacher head0.243
Teacher spread0.178 · 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
Published2023
Admission routes1
Has abstractyes

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