Análisis de costos de movientos internos de órdenes de pedidos en la operadora logística Holtrans
Bibliographic record
Abstract
The present investigation was carried out in the logistic operator HOLTRANS based on \nwhat happened in the first quarter of 2017, in the same one was realized an analysis of the \nrelated costs in the generation of the orders of the company Holtrans, this included the \nReceptions and deliveries. We reviewed a theoretical framework that helped us to make \nthe relevant calculations of the costs of the ordering process. For the accomplishment of \nthe investigation a qualitative-quantitative methodology was used the modality was of \nfield and the type of descriptive investigation of the costs and the involved variables were \nidentified. After this, a field investigation was carried out on the opinions of the workers \non the subject; Which was carried out based on an operationalization of variables, a \nhistorical documentary investigation of salaries, costs and expenses in the company was \nalso carried out.. Finally suggestions were made for the optimization of resources; And \nshowed an analysis of the costs of the current machinery versus the rental of new \nequipment. As a novelty we can cite the presentation of the evaluation of the rental model \nbased on the return on investment, which shows a financial advantage of the rental system.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".