Scholarly Trends and Rankings in Mechanical Engineering and Heat Transfer: A Global Analysis of Impact and Influence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Data generated using artificial intelligence and reported by the ScholarGPS® ranking platform are used to reveal unique scholarly trends including the impact and influence of Mechanical Engineering (ME) and Heat Transfer research. Publication and citation histories for both ME and Heat Transfer are presented for 1970–2023. A breakdown of publications is provided such as the percentage of ME publications that deal with Heat Transfer, and the percentage of Heat Transfer publications authored by ME scholars. Based on the productivity (archival publications), impact (citations), and quality (h-index) of individual scholars, the influence of countries, in both ME and Heat Transfer, is reported. Countries with growing, decreasing, and emerging influence in the last five years are identified. Top-ranked scholars and academic (and, separately, non-academic) institutions are listed for both ME and Heat Transfer. Based on their lifetime work, the world?s Top 20 Highly Ranked ScholarsTM in both ME and Heat Transfer are identified. In general, it is found that the United States and Canada, along with other developed nations have suffered significant declines in their influence in both ME and Heat Transfer research. In contrast, China, India, Iran, and other developing countries have increased their scholarly influence in both ME and Heat Transfer. University rankings follow similar trends. The methodologies used to identify the preceding trends are described in detail, so that studies of any of the 14 Fields, 177 Disciplines, and 350,000 Specialties covered by ScholarGPS can be conducted by other individuals and organizations.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it