Modelling Website Visitation and Resource Usage Characteristics by IP Address Data
Bibliographic record
Abstract
Two large website visitation data sets based on IP addresses and resource request frequency for EBSCOhost interactions were fitted to different mathematical models. Results reveal that a Zipf model provided the best fit for the site visitation data and a generalized logarithmic series model provided the best fit for the resource request data.Deux vastes corpus de données sur les visites de sites Web et basés sur les adresses numériques IP, de même que des données sur la fréquence des requêtes de ressources lors d’interactions dans EBSCOhost sont adaptés à différents modèles mathématiques. Les résultats démontrent que le modèle Zipf présente la meilleure correspondance pour les données sur les visites de sites et un modèle logarithmique général de série démontre la meilleure correspondance pour les données sur les requêtes de ressources.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.008 | 0.023 |
| Open science | 0.004 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".