New topic horizons for drone systems and applications
Bibliographic record
Abstract
Summary This journal (Drone Systems and Applications; DSA) conducted a targeted “horizon scan” during 2022 within our team of editors and associate editors. We asked— Which research areas currently under-represented in Drone Systems and Applications would you like to see more heavily represented in the future? The process highlighted five areas of interest and potential growth: Drones in the geosciences Aquatic drones Ground drones Drones within calibration/validation experiments Drones and computer vision Over the past two years (2020–22), the journal has published over 50 papers with a strong leaning towards aerial drones for ecology and also with an engineering focus. DSA is keen to receive new submissions addressing the five highlighted areas, which lie firmly within the aims and scope of the journal. Further to the horizon scan, we propose two special collections for the coming year—one addressing drone applications ( drones in geoscience applications) and a second addressing drone systems ( aquatic drone systems). We would like to hear from scientists and practitioners in these fields as both contributors and (or) collection editors.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.073 | 0.023 |
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".