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
As the growth of department stores, which have led the distribution industry, started to decrease recently, large-scale discount stores have emerged as a new format of retail business and taken the central position in the distribution industry. As large-scale discount stores gain more and more momentum in opening, there appears a shift to the competition structure between department stores and large-scale discount stores. Despite the latter`s remarkable growth, however, it should be noted that the two retail formats deal with different items in each product category and attract consumers with different preferences. Thus approaches to opening between them should naturally be different. In addition to the old approach toward opening a distribution facility including location analysis and market potential(MP) analysis, they should consider the unique characteristics of department stores to estimate sales. Thus this study divided the main variables to affect the sales of department stores into population and economic factors, location factors, internal environment factors, and differential factors. Then the investigator selected their input variables. Based on the factors to affect sales and sales data, I devised an estimation model of regression analysis and artificial neural networks. Based on the national statistics and the data of A department stores across the nation from the first quarter of 2002 to the fourth quarter of 2006, I compared the estimated and actual sales of 2007 and reviewed the model`s accuracy. In order to compare and assess total 505 cases by the regions and analysis methods, I divided the model composition into four(the Seoul metropolitan area(including Seoul), the Seoul metropolitan area(excluding Seoul), the rest of the nation, and the entire nation) and made estimations. As a result, the Seoul metropolitan area(including Seoul) model showed the highest estimating power at 96.6%. Using the model with the best estimation of sales, I predicted the sales of a new A department store for 2008. The relative importance of the input variables used in estimating sales turned out to influence the sales of a new department store. Thus it`s suggested that sales should multiply when they compose the store`s MD(merchandise) based on those variables.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.126 | 0.024 |
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