Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Since the coronavirus disease 2019(COVID-19) has had brought severe impact on all aspects of the world. A series of interpersonal distancing methods such as ensuring effective and safe social distancing among people, wearing masks, and traffic lockdown measures are also continuing to take effect to curb the continuing outbreak of the COVID-19 (“Advice for the public on COVID-19”, 2020). In response to the globally spread of COVID-19, many advanced technologies in the field of Artificial Intelligence (AI) were applied rapidly and played an essential role in the operation for several months. There are many different leading technology categories in the field of artificial intelligence and many different sub-categories within each main technology categories. Moreover, since the AGI technology does not yet reach the basic human intelligence level, this study will focus on the impact of service robots, which are already widely used in the NAI application category, on hospitality marketing in the current situation in China. In this paper the aim is to assess the effectiveness of use of service robots in Marketing Hospitality Industry during the pandemic through a quantitative study.
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.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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 it