Yi Liu Tech：Exploring Logistics Transparency (B)
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
The case consists of both A and B minor cases, which focus on decision-making optimization and service innovation business models related to logistics and big data. Case A – standing in the present and looking into the past – is a descriptive one, which mainly represents the service platform of fourth-party logistics, and is targeted at the main progress of the past 12 years made by Yiliu Technology Co., Ltd (hereinafter referred to as Yiliu Tech). It starts from opening up physical data and business process data via various software and hardware technologies. Then, through the analysis and application of logistics big data of different areas, it provides innovative data-based services including user portraits, intelligent loading and scheduling, route optimization, and logistics finance to logistics companies, carriers/fleets, drivers, and other participants on the platform. Case B, as a decision-making case of “standing in the present and looking into the future,” mainly describes how would Yiliu Tech innovate its service or business model for more stakeholders in the logistics ecosystem in the face of the transformation and upgrading of China’s logistics industry, and the strategic investment from Cainiao Network. Faced with the four possible development directions proposed in the case, how would Yiliu Tech choose and how to implement its choice?
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.016 |
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