Assessing site suitability for Scots pine using airborne and terrestrial gamma-ray measurements in Finnish Lapland
Bibliographic record
Abstract
Low-altitude airborne gamma-ray (AGR) data, obtained with the aircraft-mounted 25-l NaJ(Tl) gamma spectrometer and interpolated into a 50 × 50 m pixel size, over 1200 km2 of glaciated terrain in Finnish Lapland, were applied to classify and interpret site suitability for Scots pine (Pinus sylvestris L.). The selection of the training and validation sets for the classification of AGR data was based on the forest management history and soil moisture content (θv) determined by dielectric (ε) measurements. The ground calibration measurements showed a significant negative correlation between the soil ε (i.e., θv) and terrestrial gamma-ray flux (TGR-γ) from potassium (γK) and thorium decay series (γTh), suggesting that the attenuation of gamma flux is due to soil θv. Both ground and airborne surveys indicated that γK was significantly higher in drift of Scots pine stands than in drift of Norway spruce (Picea abies (L.) Karst.) stands. Out of four tested combinations of the AGR channels, i.e., (i) potassium (K), (ii) K and thorium (Th)/K, (iii) K, Th, and Th/K, and (iv) K and Th, K alone resulted in the best overall accuracy of 80.44% (Kappa coefficient, κ = 0.609) to classify drift materials suitable for Scots pine. The present study demonstrates that the TGR-γK and AGR-γK measurements provide a basis to delineate soil θv patterns within the site and landscape level, thus having a significant implication for the forest management planning to assess sites suitable for Scots pine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".