Aggregating Data for the Flow‐Intercepting Location Model: A Geographic Information System, Optimization, and Heuristic Framework. 截流选址模型的数据集计: 一个地理信息系统、优化和探索性框架
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
Flow‐intercepting problems have received considerable interest, represented by about 40 academic publications, since the early 1990s. Point‐based demand aggregation also has received much research interest in both industry and academia. Systematic studies of flow data aggregation for flow‐intercepting problems have not, however, been reported to date. Our research highlights the importance of flow‐based demand aggregation and develops a framework for aggregating such demand. This framework represents the first systematic study of aggregation for flow‐intercepting location models (FILM). The standard FILM is the perfect model for our goals—its aggregation errors are easy to understand and its outputs are easy to measure and compare. Our research uses geographic information systems, optimization, and heuristic technologies to examine the special network flow structure of a real‐world transportation system and to develop a comprehensive method of aggregating data for the standard FILM. We apply our method to the 2001 afternoon peak traffic data for Edmonton, Alberta (the sixth largest Canadian city) and find this application to be extremely efficient. We discover that in the Edmonton traffic flow network, a large number of paths have very small flows; major flows are concentrated in a limited number of paths; and a large number of small‐flow paths and a large number of low‐flow nodes on local streets have negligible effects on facility locations for FILM. We speculate that most real‐world transportation systems may have similar characteristics.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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