01_Phenotypic_data_per_individual_leaf.tab
Bibliographic record
Abstract
This file contains the raw phenotypic data, with one value per individual leaf, for leaf area and LMA measured on the grapevine diversity panel. “CodeVar” is the cultivar identifier, and “Cultivar.name” the full name of the genotype. “snp.imp.f.d.assign.noNA” is the genetic group (TE, table east; WE, Wine East; WW, Wine West) retrieved from Flutre et al. 2022. “LeafArea.cm2” is the leaf area in cm² and “LMA.g_per_cm2” the leaf mass per area in g per cm². “Date” is the date of measurement and “day” the corresponding day of year. “Orientation_plant” is the orientation of the plant on which the leaf was sampled and “Orientation_leaf” the orientation of the leaf itself (either SW, South West, or NE, North East). “Row” and “Position_plant” the spatial location of the plant in the experimental vineyard (row and position within the row). “"Plant_state" indicates whether the plant was assessed as holding virus or not. “Year_planted” indicates the year of obtention of the potted plant. This dataset was analysed in the R scripts “Leaf_area_analysis.html” and “Leaf_LMA_analysis.html” in order to explore the variability of LeafArea.cm2 and LMA.g_per_cm2 and to extract genotypic values (BLUPs).
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.371 | 0.296 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".