Checking In: Historical Cell Site Location Information and the Stored Communications Act
Bibliographic record
Abstract
Over the last twenty-five years, the number of cell phone subscribers in the United States has increased from slightly over 200,000 in 1985 to roughly 293 million in 2010.1 Cell phones are now the only phones used in over one quarter of all American households, a percentage that has nearly tripled in the last five years.2 Moreover, several modern phones are equipped with GPS programs that allow subscribers to accurately and easily navigate to their destination 3 using the phone that they likely have on or near their person for the majority of the day.As technology has developed over the last quarter century, the average person's life has changed through the adoption and utilization of new devices.Not only are cell phones regularly used for checking emails or updating one's Facebook status, but the Federal Communications Commission (FCC) estimates that wireless phones are responsible for about seventy percent of all 911 calls.4 In re-* J.D., May 2012, Seton Hall University School of Law; B.A., 2006, The University of Texas at Austin.I would like to thank the proprietors of El Dubs for consistently giving me gratuitous copies of their daily newspaper with my morning coffee.The Third Circuit decision that is at the heart of this Comment and which piqued my interest in this subject was brought to my attention because of their excellent customer service and appreciation.1 CTIA Semi-Annual Wireless Industry Survey, CTIA WIRELESS ASS'N,
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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 source (direct Gemma or distilled Codex), 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".