Gerenciamento de cadeias de suprimentos: análise da avaliação de desempenho de uma cadeia de carne e produtos industrializados de frango no Brasil
Bibliographic record
Abstract
Among all economics activities, agribusiness is increasing its importance with great force in the world, stimulated mainly for the growth of the population and in the demand for food. Agribusiness studies have been the focus of academic research for quite a long time. However, those studies usually have used a theoretical background, connotations, frames of reference and methodologies slightly different of those used in the research on Supply Chain Management (SCM). Most agro-industrial studies are influenced by economics theories. They usually address questions of public policy, structures of govemance and competitiveness of the industry. On the other hand, researches on SCM have a managerial concern addressing questions of operational efficiency, effectiveness, customers' needs and so on. Notwithstanding, differences on the research focus and theoretical frameworks, the systemic vision of the process - since the production of raw materials until the delivery of the product to the end consumer - is common to SCM and agro-industrial studies. The main purpose of this research is to advance in the studies of agro-industrial chains, using a management approach frequently presented in the literature of supply chain management. The unit of analysis of the research is a supply chain of poultry meat by-product in Brazil. The main approach will be the questions of management and measurement of supply chain performance.
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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".