Inventory of scientific publications on urban forestry published between 1800 and 2015: An analysis by period, topic and origin
Bibliographic record
Abstract
Research in urban forestry (UF) is rapidly evolving. In order to better understand this increased interest among the scientific community, a comprehensive inventory of scientific articles published between 1800 and 2015 on the subject was carried out. To do so, 21 keywords were used to query six different databases. Data was gathered and analyzed using the Endnote x7 reference management software. Some 3100 papers were identified and grouped by period, topic and author origin. The results show that the number of papers published has constantly risen since 1800, more so over the last decades. For example, the number of papers more than doubled between 2000 and 2009 compared to the previous decade (1990–1999). If this trend continues, the number of publications could double again between 2010 and 2019. This observation is valid for all countries, except for Scandinavian and Baltic countries where the number of related articles has decreased in recent years. The most commonly studied topics are human health and sociology, followed by air quality and pollutants. These results show, among other things, that UF research is thriving and that many scientists appear particularly preoccupied by the impacts of global warming.
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.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.104 | 0.127 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".