Bibliographic record
Abstract
This constitutes a dense and exhaustive coverage of the soybean genome using a dual genotyping approach combining both GBS and WGS for a core set of 137 Canadian soybean lines with their root system architecture (RSA)-related traits data. The entire panel was characterized via 56K GBS-derived SNPs. A Canadian soybean reference panel (102 lines, of which 56 were also present in the association panel) of 4.3M WGS-derived SNPs was used to perform imputation of untyped loci in the first catalogue. This led to a final catalogue of 2.18M SNPs (MAF ≥ 0.05) for a mean density of 1 SNP every 435 bp across the soybean genome.Automatic Root Image Analysis (ARIA) software was used to extract phenotypic data (after 10 days of growth) from the images. In total, 12 different RSA-related traits were measured from each 2D image for each soybean line including : including: total length of roots (TLR), length of primary root (LPR), length of secondary roots (LSR), distribution of total root length (DTLR), total number of roots (TNR), median number of roots (Med), maximum number of roots (Max), depth of root system (DRS), width of root system (WRS), surface of root system (SRS), diameter of primary root (DR), surface area of primary root (SAR).
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.015 |
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".