Site classification of afforested arable land based on soil properties for forest production
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".