Development of U.S. Lodging Industry and Its Implication/DEVELOPPEMENT DE L'INDUSTRIE DE LOGEMENT DES ETAT-UNIS ET SON IMPLICATION
Bibliographic record
Abstract
This article reviews the development of U.S. lodging industry and its implications, it shows the high degree of concentration in some aspects related to development of U.S. lodging industry such as market size, major players, and customer types.. And then further analyses the factors and its implications that influencing the lodging industry of US, it mainly was studied from the aspects of economic, social and technological sides. Finally forecasts the key trends of the lodging industry. Key words: Development, Influence Factor, Issue, Trend Resume: Cet article passe en revue le developpement de l’industrie de logement des Etat-Unis et son implication. Cette retrospective montre la concentration de haut niveau sous certains aspects relatifs au developpement de cette industrie tels que la dimension du marche, les acteurs principaux et les types des clients. Et puis l’article analyse les facteurs et ses implications qui influent l’industrie de logement des Etat-Unis dans les perspectives economique, sociale et technologique. Finalement, l’auteur prevoit les tendances importantes de l’industrie. Mots-Cles: developpement, facteurs influants, probleme, tendance
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".