Prediction of topsoil stoniness using soil type information and airborne gamma-ray data
Bibliographic record
Abstract
The stoniness of topsoil can have a significant impact on the cost-effectiveness and quality of work in mechanized forest operations. The operations and their models should be selected on a stand-specific basis, while the physical properties of the soil, including stoniness, to achieve maximum efficiency and to minimize the damage caused by heavy forest machinery. The aim of this study was to examine whether the stoniness of the topsoil can be predicted using the gamma-ray values available from geophysical data collected at low altitude and using soil type information. Stoniness was measured at several sites with various soil types, which were then divided into stoniness index classes (SICs) for further analysis by ordinal regression analysis using gamma-ray and soil type data. The predictions associated with SIC classification were 52% accurate and 79% acceptable (±1 class from the correct class), with kappa values of 0.55 and 0.72, respectively. The SIC prediction results were promising and showed the potential of gamma-ray and soil type data for estimating topsoil stoniness.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".