Validating One-On-One GPS Instruction Methodology for Natural Resource Area Assessments Using Forestry Undergraduate Students
Bibliographic record
Abstract
Undergraduate students pursuing a Bachelor of Science in Forestry (BSF) at Stephen F. Austin State University (SFA) attend an intensive 6-week residential hands-on instruction in applied field methods. The intensive 6-week instruction includes learning how to use the Global Positioning System (GPS) with a Garmin eTrex HCx GPS unit to accurately calculate area. Students were instructed how to assess the accuracy of their GPS collected waypoints by calculating the Root Mean Square Error (RMSE) comparing their GPS collected area measurements with instructor on-screen digitized area. Student’s average area RMSE between digitized and GPS derived area was 0.015 hectares, whereas instructor’s average area RMSE between digitized and GPS derived area was 0.015 hectares. Over 76% of students measured GPS area was within 5% of instructor on-screen digitized area. No difference between the students and instructors area RMSE of 0.015 hectares and high level of agreement between student measured GPS area and instructor on-screen digitized area: (1) indicates students receiving hands-on instruction in GPS applications can record accurate area measurements after only a limited 2 hour introduction; (2) the accuracy of the Garmin eTrex HCx GPS unit is not user dependent; and, (3) validates the interactive hands-on instruction methodology employed at SFA.
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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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".