Bibliographic record
Abstract
Tourism is one of the largest economic industries in Florida. In 2015, a record 106.3 million tourists visited Florida (about five visitors per resident), with an economic impact of about $90 billion. Tourism also provides additional benefits for federal, state, and local governments in the form of taxes (e.g., excise, sales, income, and property taxes). In Florida, tourism accounts for over one million direct jobs and an additional 1.5 million indirect and supply chain jobs. The three industries or business sectors most impacted by tourism and currently experiencing substantial growth in the state, include: leisure and hospitality (e.g., hotels, restaurants, museums, amusement parks, entertainment), transportation (e.g., cruise ships, taxis, airports), and retail trade (e.g., gas stations, retail stores). The 106.3 million tourists comprise approximately 91.2 million out-of-state visitors, 3.9 million Canadian visitors, and 11.2 million overseas visitors. The domestic visitors are anticipated to grow by 20% in 2018. Tourism and the associated industries in Florida are highly vulnerable to climate change over time. The state population and real estate markets continue to grow in the coastal areas, with corresponding increases in property values at risk. In addition, there are losses associated with the properties used to mitigate the effects of climate change. In summary, indicators of climate change, such as higher sea levels and more frequent and powerful hurricanes and other extreme weather events, have the potential to severely impact the tourism industry in Florida.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.401 | 0.124 |
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".