Structure and Trends of Worldwide Research on Durum Wheat by Bibliographic Mapping
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
The bibliometric mapping approach is a quantitative methodology to analyze the structure and evolution of research activities in a scientific area or a discipline. The objective of the current study was to perform a bibliometric analysis of the worldwide durum wheat literature published from 1961 to 2022 to identify topics and trends and their evolution over time. A total of 7512 documents were analyzed to generate bibliometric maps illustrating the main research topics. Most of the articles (91.6%) were published in indexed journals, with a low percentage (3.4%) in conference proceedings. The most active journals were the Journal of Cereal Science, Euphytica, Theoretical and Applied Genetics, Cereal Research Communications, and Cereal Chemistry. Italy, the USA, Canada, Spain, and France were the countries publishing the most documents. Research interests were focused on mutagenesis, interspecific hybridization, and technological quality in 1961–1980 and moved to conservation farming, molecular genetics, and nutritional quality in the last two decades. Future durum wheat production is facing challenges from climate change, water scarcity, and rising demand for sustainable food production. Advancements in molecular breeding techniques, genome editing, precision agriculture, and conservation farming can expedite wheat improvement and pave the way toward a healthier environment. The analysis of a large amount of bibliographic data provides useful information for researchers and policymakers and represents a starting point for a comprehensive discussion for future research.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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