Global publication trend in the field of resource recovery from wastewater: A bibliometric analysis
Bibliographic record
Abstract
ABSTRACT In the 19th century, wastewater was not given the proper attention, leading to its mismanagement and neglect. The increase in wastewater volume over time led to the adoption of treatment methods. Technological advancements have enabled a shift from wastewater treatment to resource recovery. Recently, resource recovery from wastewater has gained global attention, generating a wealth of information. Therefore, it is critical to evaluate that information as it could shed light on unexplored areas. A bibliometric analysis was conducted over 20 years, from 2002 to 2021. The study revealed that publication productivity was initially low, but there was a significant increase in productivity starting in 2013. A 5-fold increase in contributions was observed from 2013 to 2021, specifically in the number of countries. Among these countries, China and the USA were the major contributors, accounting for 50% of the publication productivity. Further, the importance of international collaboration in this field is evident, as it accounted for 40% of the publication. Wastewater is now recognized as a valuable renewable resource, rather than a liability, and continued exploration is necessary to find solutions to freshwater scarcity challenges.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.084 | 0.137 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".