Adoption patterns of autonomous technologies in Logistics: evidence for Niagara Region
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite the well-known benefits that autonomous technologies bring to supply chain and logistics, the adoption of such technologies remains a challenging task. This study seeks to investigate how firms that are associated with the generation of freight movements in Niagara Region (Canada) will respond to new autonomous technologies. Structural Equation Modeling was used to extract meaningful features from the dataset obtained from an extensive and in-depth survey. The survey was designed to help better understand firms’ views regarding autonomous technologies. The results showed that firms with a higher percentage of e-commerce sales are more likely to adopt autonomous technologies. Results also showed that third-party logistics providers are likely to play an important role in facilitating a path to adoption. The number of product lines, the number of transportation assets, and the number of import countries are other important contributing factors that can affect firms’ levels of interest in autonomous technologies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it