Logistics Solutions, Supply Chains, Climate Change, and Sustainable Development in Somalia
Bibliographic record
Abstract
The aim of this study is to provide a critical analysis of the interplay and missing links between logistics solutions, supply chains during times of climate change, and the sustainable development of logistics firms in Mogadishu, Somalia.Descriptive survey and correlational research designs were utilized, targeting 57 logistics firms in Mogadishu, where a census method was applied.The respondents, who were the units of observation, were the supply chain managers from these firms.Information was gathered from primary sources using a questionnaire that underwent a validation process and a determination of reliability before being administered.The processing of the gathered information was guided by means and standard deviations, as well as regression analysis, and the presentation was made through tables and figures.The p-values for logistics solutions, supply chains, and climate change were all less than 0.05, indicating that the variables were significant.Thus, logistics solutions, supply chains, and climate change significantly predict the sustainable development of logistics firms in Mogadishu, Somalia.Supply chain managers working in logistics firms in Mogadishu should improve their logistics solutions and supply chains by adopting modern transportation vehicles to reduce energy consumption.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".