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 economy of Pakistan relies heavily on cotton, which contributes ~60% of total foreign exchange earnings (US$ 15 billion in 2012/13). Cotton is grown on about three million hectares annually with average lint production of 670 kg ha-1. Historically the cultivation of cotton can be traced back to 6000 BC with Gossypium arboreum L. identified in the ancient remains of Monjadharo (Sindh) [1]. The indigenous cultivated cotton is locally known as Desi cotton, which carries the A-genome [2-3]. Following the industrial revolution in the textile sector, the tetraploid Gossypium hirsutum L. gradually replaced G. arboreum L., because it generally produces a higher quality lint and has a higher seed cotton yield (SCY) in the Indo-Pak region. These American types originated from New Orleans and Georgia were first introduced in 1818 [4]. This material was primarily a mixture and did not attract the interest of farmers in its initial years of cultivation because of high susceptibility to sucking insects, particularly jassids (Amarasca devastans Dist.). Organized selection procedures were adopted to select genotypes suited to the local conditions that laid a concrete foundation for breeding material on the subcontinent.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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