Standard Arpu Calculation Improvement Using Artificial Intelligent Techniques
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
abstract Recognizing how developing browsing behaviour could result in greater return for service providers through more efficient data usage without compromising Quality of Service (QoS), this paper proposes a new innovative model to describe the distribution and occurrence of behavioural errors in data usage models. We suggest: a) that the statistics of behavioural errors can be described in terms of locomotive inefficiencies, which increases error probability depending on the time elapsed since the last occurrence of an error; b) that the distribution of inter-error intervals can be approximated by power law and the relative number of errors. Comparing immersive similarities of data usage and foraging behaviours according to the Levy-Flight hypothesis, the length of the usage can be feasibly increased with less errors and eventually increase average revenue per user (ARPU). The validity of the concept is demonstrated with the aid of experimental data obtained from test software called Learn-2-Fly which sought to make browsing behaviours more efficient through user responses to stimuli created by an artificially intelligent engine. Although there were limitations on the scope of this test, a noticeable change in the user browse duration occurred over the duration of testing periods, with test subjects spending more time browsing and reacting to intended visual stimuli. The study establishes the opportunity to provide a higher quality of service to the end-user, whilst also offering a dynamic opportunity to increase revenue streams. Further consequences, refinements, and future works of the model are described in the body of the paper
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 | 0.000 |
| 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 | 0.002 | 0.000 |
| Open science | 0.000 | 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