GEOWAPP: A Geospatial Web Application for Lab Exercises in Surveying
Bibliographic record
Abstract
E-learning applications that allow students to review their survey data are not widely used in urveying Engineering. To design and develop such an application, the requirements to be studied include user inter ac tions, technology interactions, existing exercises, data representation, etc. This study comprises the process of designing, developing, and testing a geospatial web application (GEOWAPP), which is intended to be under the adjunct mode of e-learning. Four exercises were supported by GEOWAPP: two levelling exer cis es, a traversing and a topographic survey exercise. The GEOWAPP contains five tools: Traversing Comparator, Differential Levelling Comparator, Least Squares Levelling Tool, Vertical Comparator, and Proximity Comparator. After testing using surveying real data and book exercise data, the GEOWAPP func tionality was found operational. Finally, user reviews were favourable towards the GEOWAPP. This application provides a new way to support surveying exercise lab practices by delivering immediate feedback.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.100 | 0.048 |
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".