Desempeño de costos de producción de pymes a diferente escala: un análisis de casos
Bibliographic record
Abstract
In the second quarter of 2014 in Honduras 100,000 jobs will cease to exist because MSMEs will close operations due to lack of administrative capacity to remain in a competitive market. This study aimed to compare the performance of the production costs of processing MSMEs banana on a different scale, the companies studied were: micro, small and medium enterprises. Observation methodology was used to collect cost data and grouped into two types of costs: variable and fixed. The results were that the median firm has the lowest production costs, followed by small and micro enterprises finally have the highest costs. One conclusion from this study is that the banana is the main raw material in the three companies, such as buying at different prices in microenterprise the highest price and the lowest median price, not content with that micro, small and medium enterprises using different varieties of banana were the same as different yield: 34 %, 35 % and 38 % respectively. The main recommendation that the cost performance is more efficient Seek suppliers who provide the cheapest inputs, invest in equipment to labor more efficient and increase daily production to optimize the use of equipment and Directions the point of maximum production company to improve costs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".