Safety risk and operational efficiency on logistic service providers’ sustainable coal supply chain management
Bibliographic record
Abstract
Safety risk and operational efficiency in sustainable coal supply was an issue in the coal distribution supply chain. An online survey evaluating safety hazards, operational effectiveness, logistic service providers, and sustainable supply chain management was completed by 89 consumers of coal logistic service providers. In contrast, this study aimed to evaluate the operational effectiveness and safety concerns of a sustainable coal supply chain managed by the logistic service providers on the Barito River. In addition to using path analysis, quantitative research methods were also applied to calculate and process the questionnaire data, producing a value that determined the weight of each vendor, criterion, and sub-criterion. It showed several positive and significant influences connected to the safety of sustainable supply chain management, the operational efficiency of logistic service providers, supply chain management, and security risks for the providers. Therefore, it was essential to unite the positive influences on the logistic flow to secure the coal supply chain activities on the Barito River. This research would have theoretical and practical applications, adding to the advancement of science through its applications. Such knowledge would affect the safety risk and operational efficiency of sustainable supply chain management through the logistic service providers on the Barito River, Central Kalimantan.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".