Bibliographic record
Abstract
The complexity of the vapour intrusion (VI) transport pathway has received an ever-increased interest worldwide, and an improved and consolidated understanding of the VI issue requires collaboration between international research groups. This study uses the social network analysis methodology, applied to bibliometric authorship for VI research, to discover trends in collaboration, identify lead scientists, organisations, and countries. Furthermore, some of the external factors influencing the collaboration and productivity were assessed. The data suggests that the global research network for VI produced over a time span of 54 years 566 publications via 157 sources. The research network is composed of 437 organisations and 1053 authors from 33 countries. This suggests an increasingly active international collaborative research effort. However, inter-continental cooperation is much less than continental. The top five most central countries in the network are the USA, followed by Canada, China, The Netherlands, and Italy. The researchers with the most publications are from these five countries as well as the top organisations. The social network analysis conducted shows a good approximation of the collaborative structure for the key countries, organisations and researchers involved. Since 2010, the research community has become more stable.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.046 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".