A Topic Modeling Assessment of Emerging Research Trends in the Environmental Science and Engineering Discipline
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
<p>Advancements in environmental science and engineering (ESE) research is needed towards ensuring an environmentally conscious society. Understanding fundamental research developments in the ESE discipline, can help stimulate improved collaboration and communication of nascent environmental problems. Hence, in this thesis, the author applies topic modeling analysis on 3072 abstracts collected from academic journals that publish subject matter related to ESE research from 2005-2019. Accordingly, the author applies a latent Dirichlet allocation (LDA) model on abstract metadata to infer 20 trending topics. Namely, topics on environmental impact assessments, waste management, and lead pollution. Moreover, whilst quantifying the trends at the regional level, it has been observed that countries display clearly distinguishable patterns. Thus, suggesting that ESE research communities from different countries tend to specialize in various sub-fields. Environmental scientists, environmental engineers, and journal editors (among other interested parties) will benefit from the results in terms of identifying promising topics for research collaborations. </p>
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 | 0.011 | 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 | 0.001 |
| 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