Trend of R&D publications in Pressurised Heavy Water Reactors: A Study using INIS and Other Databases
Bibliographic record
Abstract
Digital databases INIS (1970-2002), INSPEC (1969-2002), Chemical Abstracts (1977-2002), ISMEC (1973-June2002), Web of Sciences (1974-2002), and Science Citation Index (1982-2002), were used for comprehensive retrieval of bibliographic details of research publications on Pressurized Heavy Water Reactor (PHWR) research. Among the countries contributing to PHWR research, India (having 1737 papers) is the forerunner followed by Canada (1492), Romania (508) and Argentina (334). Collaboration of Canadian researchers with researchers of other countries resulted in 75 publications. Among the most productive researchers in this field, the first 15 are from India. Top three contributors to PHWR publications with their respective authorship credits are: H.S. Kushwaha (106), Anil Kakodkar (100) and V. Venkat Raj (76). Prominent interdomainary interactions in PHWR subfields are: Specific nuclear reactors and associated plants with General studies of nuclear reactors (481), followed by Environmental sciences (185), and Materials science (154). Number of publications dealing with Geosciences aspect of environmental sciences are 141. Romania, Argentina, India and Republic of Korea have used mostly (=75%) non-conventional media for publications. Out of the 4851 publications, 1228 have been published in 292 distinct journals. Top most journals publishing PHWR papers are: Radiation Protection and Environment (continued from: Bulletin of Radiation Protection since 1997), India (115); Nuclear Engineering International, UK (84); and Transactions of the American Nuclear Society, USA (68).
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.075 | 0.175 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".