EVALUACIÓN DEL MODELO DE NEGOCIO DE UN RANCHO DE SOYA EN TAPACHULA, CHIAPAS
Bibliographic record
Abstract
The main economic activities of Tapachula Chiapas are agriculture, light industry, and border trade, among the main crops are coffee, cherry, mango, banana, corn, grain and soy. But according to the Municipal Development Plan 2018-2021, the primary sector is going through a structural crisis, which has caused in relation to the production of Soybeans that in the last five years it was reduced by 6.364%, having a decrease rate of 287.89 Ton per year, derived from this situation, it is decided to carry out an investigation with the objective of “Evaluating the Business Model of a soy producing company in Tapachula, Chiapas based on the Business Model Canvas and identifying its Strengths, Weaknesses, Opportunities and Threats ”for which the object of study is defined as“ Business Model ”that according to (Osterwalder & Pigneur, 2011) a business model“ describes the bases on which a company creates, provides and captures value ”. To meet the research objective and in accordance with the approach to the object of study, the type of applied research was qualitative. Once the information has been collected and analyzed, the Business Model of the Soy Ranch is defined, where it is identified that in the cycle studied this is not profitable, likewise the evaluation of the environment and the basic modules of the Business Model of the Soy Ranch is made. Soy where the main Opportunities are identified: 1. The market is demanding quality Soy that allows good yields; 2. The tropical zone where the Soconusco is located, the production band is very good due to humidity and sunlight; 3. Soy from Soconusco is of better quality than that of Illinois, which is the largest producer of Soy, and among the main threats that soy is being imported to satisfy the country's demand; Soy production is declining due to lack of profitability and soy customers can import it through the TMEC (Treaty between Mexico, the United States and Canada).
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".