Final Review of the Application of the SCOR Model: Supply Chain for Biodiesel Castor – Colombia Case
Bibliographic record
Abstract
This paper presents the final analysis on the process of characterizing the supply chain for Biodiesel Castor, made with the application of the SCC, SCOR Model, showing the strengths and weaknesses in the design of a strategic plan and logistics, as an important aspect in the search for alternatives, where tools such as SCOR and other applications or methodologies to characterize supply chains, may provide support for effective decision making, which somehow guarantees progress and development of production systems. This application, as a methodological tool, led to the identification of the different variables and operations that make up the supply chain processes such as obtaining Biodiesel from Castor, which determined what are the KPI´s of this chain, a determining factor for the validation of logistics Biodiesel process operations in order to strengthen and identify disconnects. In an increasingly globalized world, where the strength and competitiveness are defined by the effective management of the supply chain that enables better delivery of customer service and value chain through the management of information flows, product and financial flows, such management potentiates the compete successfully in today's markets, for the result produced by the combination of the objectives of supply chain and implementing best practice methodologies.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".