Information mining analysis of PM2.5 literature in foreign language
Bibliographic record
Abstract
Objective To explore the information distribution of PM2.5 Paper in Foreign Language. Methods This article took PM2.5 papers as research object, used GoPubMed as information search and statistical analysis tool to conduct information mining of PM2.5 literature, including subject, time, author, source, distribution and number of publications over time. Results There was steady development of PM2.5 literature published in foreign language from 2004 to 2012, dramatic increase during the year 2013 to 2015. The three top publishing years of PM2.5 literature were 2015, 2014 and 2013. Main authors of such literature include Chow J, Watson J, Koutrakis P and so on. Main research countries include United States, China, Canada, etc. Big cities, like Beijing, Shanghai, Boston were main places conducted such PM2.5 research. Related articles mainly published in journals such as J Air Waste Manag Assoc, Environ Sci Technol, Sci Total Environ and so on. Six out of the top ten research journals have higher impact factors than the average level of all journals of SCI in 2014. Main research terminologies include particulate matter, air pollutants, particle size and so on. Conclusions Generally, the PM2.5 literature over the world has a high level academically and China plays a significant role in such research. Key words: PM2.5; Literature in foreign language; Information mining
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 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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.073 | 0.044 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".