Precise marketing of precision marketing value chain process on the H group line based on big data
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 frequent trading activities of electronic commerce make the online transaction volume of Chinese enterprises increase year by year, but many enterprises still follow the traditional marketing strategy, which is not conducive to the long-term development of enterprises. Online precision marketing system model based on big data was built, Hadoop + MapReduce precision marketing model platform was implemented, all the data were stored in a distributed storage system, data mining technology was used to deal with it and provide the basis for enterprise decision making. China’s H group was studied. The “user portrait database” and the corresponding E-R map were constructed. The height subdivision factor with strong correlation was selected for cluster analysis, and the product was subdivided by cluster analysis. This study has certain reference significance for the collection and mining of online data of enterprises in our country and contributes to the long-term healthy development of the enterprise.
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 | 0.016 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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