Ecosystems and History of Evolution and Spread of Sugar Producing Plants in the World-an Overview
Bibliographic record
Abstract
Plants used for making sugar differ from ecosystem to ecosystem of the world and history has played a great role in their spread. People in the tropics and sub-tropics (Papua New Guinea, China, and India) were the first to domesticate sugarcane, numerous sugars producing plant in the world. ICAR–Sugarcane Breeding Institute, Coimbatore in India was the first to do the breeding work in sugarcane and the Coimbatore canes or varieties developed from them dominate the sugarcane producing areas in the world. Indians first made jaggery (Gur) by concentrating the juice by boiling it and cooling it in earthen pots. They were also the first to develop crystal white sugar producing technology in the beginning centuries of CE. Of course, it was later improved by the British, who dominated the European sugar market. Europeans, specially Poles, Germans and French domesticated sugar beet and developed technology for making sugar from it. Aboriginal in North America were the first to develop the technology for making sweet syrup from maple tree and the migrants from Europe then further improved it. Canada is the world’s leading country in exporting maple syrup today. People in the southern states of US developed sweet sorghum for making sorghum syrup. Corn producers in USA developed the technology for making corn syrup.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".