Publisher Correction: Genomic prediction for hastening and improving efficiency of forward selection in conifer polycross mating designs: an example from white spruce
Bibliographic record
Abstract
Author notes These authors contributed equally: Patrick R. N. Lenz, Simon Nadeau Authors and Affiliations Natural Resources Canada, Canadian Wood Fibre Centre, 1055 rue Du PEPS, P.O. Box 10380, Québec, QC, G1V 4C7, Canada Patrick R. N. Lenz, Simon Nadeau & Marie Deslauriers Canada Research Chair in Forest Genomics, Institute of Systems and Integrative Biology, and Centre for Forest Research, Université Laval, 1030 Avenue de la Médecine, Québec, QC, G1V 0A6, Canada Patrick R. N. Lenz, Aïda Azaiez, Sébastien Gérardi, Martin Perron, Nathalie Isabel, Jean Beaulieu & Jean Bousquet Ministère des Forêts, de la Faune et des Parcs, Gouvernement du Québec, Direction de la recherche forestière, 2700 rue Einstein, Québec, QC, G1P 3W8, Canada Martin Perron Natural Resources Canada, Laurentian Forestry Centre, 1055 rue Du PEPS, P.O. Box 10380, Québec, QC, G1V 4C7, Canada Nathalie Isabel Authors Patrick R. N. Lenz View author publications You can also search for this author in PubMed Google Scholar Simon Nadeau View author publications You can also search for this author in PubMed Google Scholar Aïda Azaiez View author publications You can also search for this author in PubMed Google Scholar Sébastien Gérardi View author publications You can also search for this author in PubMed Google Scholar Marie Deslauriers View author publications You can also search for this author in PubMed Google Scholar Martin Perron View author publications You can also search for this author in PubMed Google Scholar Nathalie Isabel View author publications You can also search for this author in PubMed Google Scholar Jean Beaulieu View author publications You can also search for this author in PubMed Google Scholar Jean Bousquet View author publications You can also search for this author in PubMed Google Scholar Corresponding author Correspondence to Patrick R. N. Lenz .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.091 | 0.047 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".