Evaluating Interactive Transect Area Assessments Hands-On Instruction for Natural Resource Undergraduate Students
Bibliographic record
Abstract
Undergraduate students pursuing a Bachelor of Science in Spatial Science degree at Stephen F. Austin State University (SFASU) receive instruction in the spatial sciences with a focus on hands-on applications. All undergraduate students take the course Introduction to Spatial Science which includes a comprehensive overview of spatial science incorporating a comparison of standard inexpensive area assessment techniques with high-end computer based area assessment methodologies. Students within this course were instructed how to assess the area of a surface feature on an aerial image with a ruler applying the transect method. Student’s average Root Mean Square Error (RMSE) between a student’s transect derived area and surveyed area was 0.45, 0.13, 0.07, and 0.02 acres for 5, 10, 15, and 20 transect lines, respectfully. An ANOVA analysis of area assessment and area error assessment indicated that mean area and mean absolute area error were statistically different between transect line groups. In addition, the accuracy of area measured increased significantly as the number of transect lines increased which: (1) indicates students receiving hands-on instruction in estimating area using the transect method can record accurate area measurements after only a limited 2 hour introduction; (2) reveals the accuracy of the transect method is not user dependent; (3) shows area accuracy increases as the number of transects increases; and, (4) validates the interactive hands-on instruction methodology employed at SFASU.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".