Preface to the AAQR Special Issue “Fog, Fog Collection and Dew”
Bibliographic record
Abstract
Fog is a “suspension of very small, usually microscopic water droplets” that “reduce horizontal visibility at the Earth’s surface to less than 1 km” (WMO, 2017). Fog may also be considered as a cloud in contact with the Earth’s surface. Dew is a “deposit of water drops on objects, produced by the direct condensation of water vapour from the surrounding air” (WMO, 2017). Both fog and dew formation are driven by the condensation of water vapor to liquid water in the very lowest part of the atmospheric boundary layer, i.e., in association with air masses with terrestrial or marine surface contact. Fog as a phenomenon is the object of various science and engineering fields such as meteorology, transportation safety, hydrology, and biology. Fog can scavenge airborne pollutants in urban and industrial areas, creating a health hazard, but can also deliver nutrients to natural environments. As part of the ecohydrology of natural systems around the world, fog creates unique endemic species distributions. Through its unique impact on humans’ perception of the environment, fog has also found its way into literature and the art of painting. In some areas of the world, fog is even utilized as a valuable source for freshwater production. To lesser degree, this is also true for dew, which may be collected with the aim to generate potable water. The triannual International Conference of Fog, Fog Collection and Dew started some 20 years ago in Vancouver (Canada, 1998), went on through St. John's (Canada, 2001), Cape Town (South Africa, 2004), La Serena (Chile, 2007), Münster Germany, 2010), Yokohama (Japan, 2013), Wrocław (Poland, 2016), and will be continued in Taipei (Taiwan) in 2019. This special issue of “Aerosol and Air Quality Research” (AAQR) is a selection of contributions as presented at the most recent 7th International Conference of Fog, Fog Collection and Dew at the University of Wrocław from 24 through 29 July, 2016. Of the 162 contributions to the conference, 33 were submitted to AAQR as manuscripts to be included in this special issue. Twenty one were accepted after a peer-review process and guest editors’ decisions.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".