Towards Improving Visibility Forecasts in Taiwan: A Statistical Approach
Bibliographic record
Abstract
Re duced vis i bil ity is a ma jor safety con cern at air ports lead ing to flight de lays or di ver sions. The pri mary mo ti va tion for this study is to en hance un der stand ing of vis i bil ity and to build a sim ple, prac ti cal vis i bil ity fore cast ing method across Tai wan in an op er a tional set ting by us ing readily avail able, ground-based observations. This pa per pres ents for the first time a sys tem atic, quan ti ta tive ex am i na tion of the con trols on vis i bil ity over the en tire Tai wan re gion by adopt ing a sta tis ti cal ap proach re lat ing vis i bil ity to var i ous phys i cal vari ables. A mul ti ple lin ear re gres sion is car ried out for the early morn ing hours dur ing the months of November ~ April, when vis i bil ity is es pe cially low. The re gres sion re veals that on the west coast of Tai wan, the con cen tra tion of fine par ti cles (PM 2.5 ) and rel a tive hu mid ity (RH) are most re lated to vis i bil ity, and to a lesser ex tent, coarse par ti cles (PM 10-2.5 ) and windspeed. The sig nif i cantly el e vated PM con cen tra tions would, there fore, cause a marked re duc tion in vis i bil ity on Tai wan's west coast -where most of Tai wan's pop u la tion and anthropogenic PM emis sions are found. Vis i bil ity on the east coast ap pears to be con trolled by some what dif fer ent mech a nisms, with rain fall play ing a larger role. The prob a bil ity of oc cur rence of es pe cially low vis i bil ity ( 1600 m) was re vealed by lo gis tic re gres sion to be the most sta tis ti cally re lated to RH, and, to a less extent, to PM concentrations.
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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 | 0.004 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".