Crop Information Engine and Research Assistant (CIERA) for managing genealogy, phenotypic and genotypic data for breeding programs
Bibliographic record
Abstract
Abstract Background With the advent of next-generation marker platforms and phenomics in crop breeding programs, the volume of both the genotypic and phenotypic data produced has increased exponentially. Often the data remain underutilized if not properly collated, managed and accessed. Effective management of the data is paramount to making sound and timely decision on cross planning in order to accelerate genetic gain (ΔG) in crops for disease resistance, agronomic and end-use quality traits. Results To address the challenges in managing and efficient utilization of the sheer volume of data generated in a crop breeding program, we developed an electronic information system called the Crop Information Engine and Research Assistant (CIERA). The CIERA, written in Visual Basic, runs on the Microsoft Windows operating system and requires the .Net Framework 4.7 as well as the MySQL Community Server 5.7. The highly intuitive graphical user interface of CIERA includes user-friendly query tools to facilitate the collation of data across relevant phenotypic environments from its phenotypic data management database and can combine that information with the genealogy and genetic data from its genealogy management and genetic data management databases, respectively. Conclusions Using CIERA, breeders can build a comprehensive profile of germplasm, within a few minutes, to assist them in planning crosses for enhancing genetic gain by selecting superior lines for crosses.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.023 |
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".