Annual report 2014: CGIAR Research Program on Roots, Tubers and Bananas
Bibliographic record
Abstract
Supporting Collaboration to Confront TR4: the Latest Threat to Bananas in Africa 50 Learning Alliance Creates Synergies to Help Farmers Beat BBTD in Africa 50 Assessing Disease Threats to Taro and Cocoyam in West & Central Africa 52 Supporting Efforts to Control Emerging Threats to Cassava in Asia 53 Promoting a Farmer-Friendly Method to Control Banana Xanthomonas Wilt (BXW) 53 Making Available Low-Cost, High-Quality Planting Material for Farmers 57 New Technologies Developed to Boost Yam Seed Production in West Africa 57 Building Capacity to Produce Quality Seed Potatoes for Farmers in Tanzania 58 Clean Planting Material to Combat Banana Moko Disease in Latin America 59 Developing a Cross-Crop Seed Systems Framework 59 Improving Cropping Systems and Postharvest Technologies 64 Developing Tools for more Productive, Ecologically Robust Cropping Systems 64 Keys to Improving Plantain Yields for Smallholders in West and Central Africa 64 Boosting Yam Productivity Through Intercropping and Soil Enhancement 66 Promoting Postharvest Technologies, Value Chains and Market Opportunities 69 Uganda Postharvest Project to Test Value Chain Innovations for RTB Crops 69 Measuring the Environmental Impact of Cassava Processing 71 RTB and PIM Collaborate to Make Value Chain Work Gender-Responsive 72 Searching for Technologies to Improve Traditional Cassava Processing 73 CIP
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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.031 |
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".