Using a Reference Color Plate to Correct Smartphone-Derived Soil Color Measurements with Different Smartphones Under Different Lighting Conditions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Soil color has long been used as an indicator for soil properties such as soil organic carbon and soil moisture. Recent developments in citizen science have seen the increased use of smartphone cameras for soil color measurements. However, there are high errors associated with this technique. Two major sources of errors are smartphone cameras and lighting conditions. These errors limit the applicability of this technique in citizen science. Existing correction methods for reducing these errors are either ineffective or too complicated or difficult to apply. There is also a lack of systematic analysis on how these correction methods can reduce errors. In this study, we tested the effectiveness of using a color plate as a reference to reduce the errors on color measurements due to the use of different smartphones and taking photos under different lighting conditions. Three types of objects were tested, including the squares on the color plate itself, the color chips in a Munsell soil color book, and soil samples. The results show that the raw values of color parameters showed different patterns of biases with different smartphones and lighting conditions. The calibration reduced the errors consistently for all smartphones under all lighting conditions for the color plate squares. For the Munsell book chips or the soil samples, the calibration did not always reduce the bias but it did reduce the variations in all color parameters among smartphones and lighting conditions and, therefore, improved the precision of color measurements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it