Bibliographic record
Abstract
Preface U.S. Travel & Industry Travel & Spending Grows in Second Quarter 2010 International Visitation to the United States: A Statistical Summary of U.S. Arrivals (2009) Top 10 International Markets: 2009 Visitation & Spending Testimony of Mary Saunders, Acting Assistant Secretary for Manufacturing & Services, International Trade Administration, Dept. of Commerce, before the Senate Commerce, Science & Transportation Subcommittee, Hearing on in Troubled Times Testimony of Jay S Witzel, President & CEO, Carlson Hotels Worldwide, before the Senate Commerce, Science & Transportation Subcommittee, Hearing on The National Economic Impact of Travel & Tourism Testimony of Sam Gilliland, Chairman & CEO, Sabre Holdings Corporation, before the Senate Commerce, Science & Transportation Subcommittee, Hearing on in Troubled Times Testimony of Jay Rasulo, Chairman, Walt Disney Parks & Resorts, before the Senate Commerce, Science & Transportation Subcommittee, Hearing on in Troubled Times Testimony of Rossi Ralenkotter, President & CEO, Las Vegas Convention & Visitors Authority, Las Vegas, Nevada, before the Senate Commerce, Science & Transportation Subcommittee, Hearing on in Troubled Times Restoring America's Travel Brand: National Strategy to Compete for International Visitors Index.
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 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".