Development Of Equation Of Curve To Estimate Volumetric Soil Moisture Content In CaseOf Tensiometer To Overcome The Moisture Measurement Error Due To Time Lag Issues
Bibliographic record
Abstract
A field experiment was carried out at the Instructional Farm of Bidhan Chandra krishi viswavidyalaya, located in Mohanpur, Nadia, West Bengal, in order to explore the variations in estimation of soil moisture due to time lag issues in case of tensiometer. In this work, the gravimetric technique and tensiometers were used to calculate the volumetric soil moisture content. With regard to the gravimetric approach, which was considered as the reference method, the variation of volumetric soil moisture content derived from tensiometer was analysed. For the tensiometer, it was calculated that the coefficient of variation (CV) and standard deviation (STD) values were 0.213 and 6.95 respectively. The tensiometer data was processed to compare with that volumetric moisture data which was obtained via the gravimetric approach. The objective of this study is to generate an equation of curve which would estimate the actual (error-free) volumetric soil moisture content for tensiometer from the measured volumetric soil moisture content. The equation of the curve, y = 0.009x2 + 0.7786x + 7.0652, was generated by plotting volumetric soil moisture content under tensiometer against volumetric soil moisture measured by gravimetric method (observed data). As tensiometer is not capable of producingcorrect moisture values from known tension values using soil moisture characteristics curves, due to time lag issues, soin determining the correct soil moisture status, the method of development of the equation is necessary and was proved to be very much fruitful whenever this instrument was used in the field.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".