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
Aiming at the airport taxi scheduling problem, the taxi driver selection decision-making model and optimization probability model are established. Read the network data through Java program, and then use Matlab to analyze the data. The accuracy and rationality of the model can be judged by fitting the real data. This paper explores the influencing mechanism of factors related to taxi driver's decision-making, and establishes a decision-making model for taxi drivers to choose different schemes. Determine the waiting time of taxi drivers according to flight information, season and time period factors. Under the condition of ensuring the safety of vehicles and passengers, the scheme of putting passengers into two parallel loading zones reasonably is worked out, which makes the total riding efficiency the highest. In order to ensure the revenue balance among taxis, taxi drivers should give priority to the taxi drivers. The probability density function is introduced to establish the probability model, so that the taxi driver can get the same mathematical expectation of the revenue per unit working time whether it is a long-distance guest or a short-distance guest.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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