Symposium: Diagnostics in the Age of Genomics
Bibliographic record
Abstract
What's new in plant pathology diagnostics? N. Boonham. Central Science Laboratory, York, YO41 1LZ, UK Potential of microfluidic systems for diagnostics in plant pathology. S.A. Hashsham. Edwin Willits Associate Professor, Department of Civil and Environmental Engineering and the Center for Microbial Ecology, A126 Research Complex-Engineering, Michigan State University, East Lansing, MI 48824 USA. Chaperonin-60 sequences and the cpnDB reference database: diagnostic tools for phytopathology. J.E. Hill. Department of Veterinary Microbiology, Western College of Veterinary Medicine, University of Saskatchewan, 52 Campus Drive, Saskatoon, SK S7N 5B4, Canada. Loop-mediated isothermal amplification technology for rapid detection of plant viruses. D. James. Sidney Laboratory, Canadian Food Inspection Agency, 8801 East Saanich Road, Sidney, BC V8L 1H3, Canada. Fungal species “expression” with oligoarrays and RTPCR. C.A. Lévesque. Eastern Cereal and Oilseed Research Centre, Agriculture and Agri-Food Canada, 960 carling Avenue, Ottawa, ON K1A 0C6, Canada. Genome polymorphism in cyst-forming Globodera and Heterodera nematodes. M. Madani and S.H. De Boer. Charlottetown Laboratory, Canadian Food Inspection Agency, 93 Mount Edward Road, Charlottetown, PE C1A 5T1, Canada.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.058 | 0.034 |
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".