Bibliographic record
Abstract
The fiber crops industry is one of the country's major pillars in employment generation and foreign exchange earnings. However, recent trade developments and local production problems in the fiber crops industry might affect its long-term sustainability and viability. The reduction of trade barriers under the GATT-WTO implies that in order for the Philippines to be globally competitive, the country must exert all efforts to increase the productivity of Philippine fiber crops, lower the cost of production, and improve the quality of fiber and fiber products through technological developments. In recent years, the increasing share of Ecuador in the world market is threatening the Philippines position as the top producer of abaca. Abaca farmers in Ecuador are mechanizing and producing consistent quality fibers. Unless the weaknesses and threats in the abaca industry are faced, the country's market share in the world market for abaca fiber will continue to diminish. This paper, therefore, aims to present an industry profile with focus on domestic production, consumption, external trade, problems/constraints, and market potentials; review past researches on fiber crops, technologies generated, and the extent of participation of the private and public sectors; identify research and technology gaps for the fiber crops industry; identify strengths and weaknesses in the institutional structure of research and extension interface, as well as research complementation efforts; and suggest recommendations and R & D agenda for the fiber crops industry.
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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.009 |
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".