Global evolution of research on autohydrolysis (hydrothermal) pretreatment as a green technology for biorefineries: A bibliometric analysis
Bibliographic record
Abstract
Based on 6 403 research articles in the Web of Science database from 2000 to 2023, information visualization technology is employed to analyze the literature year distribution, keyword co-occurrence and research hot spots, author cooperation network, institutional and national cooperation network, published journals, and co-cited literature in the middle of hydrothermal pretreatment . Our results show that the number of applied research publications related to hydrothermal pretreatment has increased every year in the past two decades. Among these publications, China (36.5%) is the most active country in the world, followed by the United States (14.6%) and Japan (8.2%), with increasing global cooperation. The Chinese Academy of Sciences ranks first among the institutions in the light of total publication output (245 articles, accounting for 3.82%). Among 955 journals, Bioresource Technology is cited the most frequently. The study is centered on the enhancement and potential evolution of lignocellulosic biomass raw materials via hydrothermal pretreatment for subsequent bioenergy transformation. Concurrently, the domain of hydrothermal pretreatment has progressively become more cross-disciplinary, intertwining with the sectors of microbial populations and genomes.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.104 | 0.173 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".