Citation patterns of publications using unmanned aerial vehicles in ecology and conservation
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) are incorporated as an important part of the toolbox to complement existing methods used in studies in ecology. It is therefore useful to understand how publications concerning those studies accumulate citations over time. In this study I used 213 articles in which UAVs were used in the research and I investigated for potential factors underlying how many citations they received. I used metrics that were already shown to be correlated with the number of citations in other fields, and tested more specific effects, such as the ecosystem, habitat type, or the International Union for Conservation of Nature (IUCN) Red List status of the study species. I found that the time elapsed since publication was the only variable explaining the number of citations a publication received. The average number of citations was 12.1 [95% credible intervals: 8.8–16.7] after 2 years and 41.8 [95% credible intervals: 27.1–63.7] after 5 years. In total, <6% of publications had no citations after 1 year and <0.5% of publications after 2 years, which is lower than for the field of biology as a whole. This study allows a baseline to be established, from which we can compare the evolution of the field in the future.
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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 | 0.013 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.039 | 0.063 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".