Curated and Analysed Historical Barley (Hordeum sps.) Phenotypic Data from seven European genebanks
Bibliographic record
Abstract
This dataset compiles historical phenotypic records of Barley (Genus: Hordeum) accessions evaluated across multiple years and locations by seven European genebanks participating in the AGENT project. The data originate from long-term evaluation trials conducted under diverse agro-ecological conditions and represent one of the most comprehensive multi-environment phenotypic collections of barley genetic resources in Europe. The dataset includes raw phenotypic data, outlier-corrected data, and best linear unbiased estimates (BLUEs) for a wide range of agro-morphological and developmental traits. Traits typically include, but are not limited to, days to heading (DTH), plant height (PH), thousand kernel weight (TKW), and other agronomic descriptors recorded according to national or genebank-specific descriptors. The outlier-corrected dataset represents cleaned phenotypic data after statistical identification and removal of erroneous values using a mixed-model approach. The BLUE dataset provides adjusted means per accession, accounting for design and replication effects, and serves as a robust input for downstream analyses such as genomic prediction and genotype x environment interaction studies. The author gratefully acknowledges the teams from IPK, INRAE and the CARC genebank for their valuable support and guidance, as well as the collaboration of all AGENT genebanks contributing to this study. References: 1) Bernal-Vasquez, AM., Utz , HF. & Piepho, HP. Outlier detection methods for generalized lattices: a case study on the transition from ANOVA to REML. Theor Appl Genet 129, 787–804 (2016). https://doi.org/10.1007/s00122-016-2666-6 2) Philipp N, Weise S, Oppermann M, Börner A, Graner A, Keilwagen J, Kilian B, Zhao Y, Reif JC and Schulthess AW (2018) Leveraging the Use of Historical Data Gathered During Seed Regeneration of an ex Situ Genebank Collection of Wheat. Front. Plant Sci. 9:609. doi: 10.3389/fpls.2018.00609
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".