Assessing Drone Mapping Capabilities and Increased Cognitive Retention Using Interactive Hands-On Natural Resource Instruction
Bibliographic record
Abstract
The use of Unmanned Aerial Systems (UAS), also known as drones, is increasing in geospatial science curricula within the United States. Four geospatial science faculty members within the Arthur Temple College of Forestry and Agriculture at Stephen F. Austin State University (SFASU), Texas, focus on applying imagery obtained from drones to map, monitor, and quantify natural resources. To produce society-ready foresters, natural resource managers, and environmental scientists, the geospatial science faculty employ an intensive one-on-one hands-on interactive approach in training future resource management professionals in how to effectively apply drone technology within natural resource endeavors. In particular, recent instruction has focused on training students how to evaluate the amount of overlap and sidelap percentages required within a drone flight to create the optimum orthophoto mosaic. Results indicate that the one-on-one interactive methodology employed by faculty at SFASU produce highly qualified drone pilots capable of providing the drone community with new insights on how to produce accurate orthophoto mosaics in a timely and efficient manner.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".