Site classification of afforested arable land based on soil properties for forest production
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
We used discriminant analysis to identify the key soil properties that discriminate among 30 forest sites representing a floristic site-type fertility gradient. Thereafter we classified 24 sites on afforested arable land into forest site types using these discriminant functions. The most important soil properties of the E horizon separating different forest site types were pH and the densities of Ca, P, and silt. Using properties of soil from the 010 cm depth to represent the current level of site productivity, we classified all soils from afforested arable land into forest site types of high productivity. Among these soils, the most fertile were those with high clay and silt densities. Again, using properties of soil from the 3040 cm depth to emulate the site productivity that prevailed before soil-formation processes and agricultural land use altered the upper soil horizon, we classified most of the soils from afforested arable land into forest site types of medium productivity. This implies that agricultural land use had increased the densities of basic elements at the 010 cm soil depth and, consequently, site productivity. The high productivity of former arable lands was attributed to their previous agricultural use and to the inherent properties of fine-grained soils.
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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 | 0.000 |
| 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