Towards a Reference Architecture for Cloud-Based Plant Genotyping and Phenotyping Analysis Frameworks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The domain of plant genotyping and phenotyping presents a number of challenges in the area of large data computation. Various tools and systems have been developed to automate the scientific workflows and support the computational needs of this domain. In this paper, we review a number of the popular systems (i.e., Galaxy, iPlant, GenAp and LemnaTec) in the domain of plant genotyping and phenotyping using the scenario-based architectural analysis method (SAAM). In particular, we focus on how different stakeholders are using these systems in a variety of scenarios and to what extent the systems support their needs. Our SAAM analysis shows that the existing systems have shortcomings. For example, they are limited in their support for high throughput processing of large amounts of heterogeneous types of data. Based on our findings we propose a reference architecture along with a preliminary evaluation in the subject domain. The reference architecture and its evaluation is aimed at helping developers/architects create suitable architectural designs and select appropriate technologies when developing plant phenotyping and genotyping systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it