The Agro-Industrial Sugarcane System in Mexico: Current Status, Challenges and Opportunities
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
Sugarcane cultivation in Mexico occurs under a wide range of socioeconomic, environmental and agricultural conditions, with the last three harvests (2010/2011, 2011/2012 and 2012/2013) providing yields ranging from 36-125 t ha-1 (variation > 347%), with an average yield of 70.2 t ha-1, which is below the world average of 80 t ha-1. The total area allocated to sugarcane production in Mexico is close to 800 thousand hectares, and could rise to nearly 5 million hectares given adequate conditions for its cultivation. This activity generates approximately 1 million direct jobs, 2.2 million indirect jobs, and more than 2.5 billion dollars (0.4% of GDP) per year. Climate change and the rapid market penetration of high fructose corn syrup are among the greatest threats to this agribusiness, including severe disintegration of production processes in the field, industry, commerce, and consumption of cane sugar. Technology lags, low investment, high processing costs and shortcomings in production sales are issues the industry must address by leveraging their resources and coordinating processing links to be more efficient and competitive. Political influence has imposed a suboptimal policy framework to achieve the projected potential. To overcome current lags in the field and refineries within the country, significant innovations across the value-chain are underway, including a robust breeding program, digitalization of sugarcane fields and novel investments in research and development. The sugarcane value-chain has great potential for Mexico, and exploiting this potential is possible if technological, organizational and commercial management innovations currently in progress in fields and factories are applied.
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 | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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