Evaluating Patterns of Research Activity in Terrestrial and Aquatic Ecological Corridors using a Systematic Quantitative Literature Review (SQLR) Framework
Bibliographic record
Abstract
This study investigates emerging patterns and trends of research activity in terrestrial and aquatic ecological corridors from 1991 to 2022. A Systematic Quantitative Literature Review (SQLR) framework was applied to analyse existing literature across multiple scientific databases. Topic modelling, co-occurrence networks and occurring phraseologies were used as text mining approaches to provide quantitative analysis to the review. Research was centred around carefully selected terms using an inclusion and exclusion criteria to filter out unrelated articles while abstracts and author keywords were extracted and analysed for text mining. The results of SQLR revealed the total number of publications and co-authorship have increased over time. Research approaches were largely observational but show a shift towards a mixture of observational and experimental techniques. Canada, China, Europe and the United States produced a large proportion of papers which also reflects the type of biomes and organisms analysed. Of the species identified in publications, large mammalian organisms were commonly studied in comparison to herpetofauna suggesting patterns of bias based on levels of endemism and evolutionary uniqueness. Topic models and co-occurrence networks show wildlife dispersal structures and spatial conservation to be prominent topics in research. Such results suggest the trajectory of spatiotemporal research and species occupancy models in ecological corridors. Finally, future research highlights the work of collaboration among researchers across disciplines creating more novel scientific discoveries and evidence-based research.
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How this classification was reachedexpand
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".