KARST IN URBAN AREAS
Bibliographic record
Abstract
In its third edition, the “William Morris Davis – Journal of Geomorphology” presents its second interview with geographers, to head the “Interviews” section, which opens each published issue. This time, it is the first international interview, carried out with Professor Philip van Beynen, from the University of South Florida, in the United States. Professor Philip van Beynen was interviewed on the topic “Karst in Urban Areas”, and brings important data on the subject, with beautiful illustrations and with examples from all over the world. The interview took place on September 17, 2020, with the participation of Vanda de Claudino-Sales (Professor of the Academic Master in Geography at the State University of Vale do Acarau-UVA) and Saulo Roberto Oliveira Vital (Professor of the Department of Geography and the Post-Graduate Program in Geography at the Federal University of Paraiba - UFPB), and was transcribed by Diego Nunes Valadares, master's student on Geography at the Federal University of Rio Grande do Norte. Professor van Beynen was born in New Zealand, where he received his degree in Geography at the University of Auckland. He earned a master's degree from the same university, and a doctorate and post-doctorate from McMaster University, Canada. He has been a professor at the School of Geoscience at the University of South Florida since 2009, where he has been developing research related to different components of karst environments. The interview shows his great expertise on the subject, and is very much worth to be read and seen even for those who are not specialists in karst.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".