The Linkcell Construct and Location-Aware Query Processing for Location-Referent Transactions in Mobile Business
Bibliographic record
Abstract
The technologies that enable the transactions and interactions of mobile business are now as ubiquitous as any business-applicable technology that has emerged in recent decades. There is also an exploding base of literature with mobile business as its subject. The variety and volume of literature present a challenge to defining mobile business (m-business) in a way that differentiates it from other forms of technology-enabled business activity. For purposes here, m-business is held to be an extension of electronic business wherein transactions occur through communication channels that permit a high degree of mobility by at least one of the transactional parties. Within m- business, the distinct sub-area of locationbased mobile business (l- business) has recently emerged and is rapidly expanding (Frost & Sullivan, 2006). In l-business, the technologies that support m-business transactions are extended to incorporate location-aware capabilities. A system is ‘location aware’ when it senses a transactional party’s geographical position and then uses that positional information to perform one or more of the CRUD (create, retrieve, update, delete) functions of data management in support of a mobile user’s transactional activities (Butz, Baus, and Kruger, 2000). In their discussion of “location awareness”, Yuan and Zhang (2003) suggest that it “is a new dimension for value creation” applicable to an extensive variety of areas in which mobility is a salient characteristic: travel and tourism, commercial transportation, insurance risk/recovery management, emergency response, and many others.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".