Development of a Depth Sensing Device and a Utility Software for a Dynamic Penetrometer
Bibliographic record
Abstract
A system for sensing depth of penetration of a dynamic penetrometer was developed. It contained an ultrasonic distance sensor working together with a microcontroller capable of sensing and recording distance of penetration and field data necessary for the construction of the penetration resistance (PR) profile. The system was equipped to the conventional dynamic penetrometer of the Department of Soil Science, Kasetsart University at Kampangsan. The depth sensing system was found to work properly. Depth validation by means of t-test yielded t value of 0.1713 as compared to 0.9800 of t-table under two-tailed test. In addition the RMSE and the MAPE values were 1.2625 and 3.3473 %, respectively. Validation test on depth of penetration showed satisfactorily insignificant difference between depths read by the invented device and by standard measurement. A computer program for manipulating field data for PR calculation was constructed. The program was written in Microsoft Visual Basic and imbedded into Microsoft Excel spreadsheet capable of accessing the field data being recorded in an SD card of the ultrasonic distance sensing system. The data was manipulated by means of a user interface that enabled the user to construct the PR profile with ease.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".