Site index of Sitka spruce (<i>Picea sitchensis</i>) in relation to different measures of site quality in Ireland
Bibliographic record
Abstract
To examine the relationships between Sitka spruce (Picea sitchensis (Bong.) Carr.) site index and site quality variables, we sampled 201 Sitka spruce stands covering the entire range of sites supporting the growth of the species in Ireland. Site index varied significantly with climate and climate surrogate variables, some site quality variables, soil physical and chemical properties, edatopes (combinations of soil nutrient and moisture regimes), rotation types, provenance, and fertilizer regimes. We developed a series of models to predict site index using climate, site, soil physical and chemical properties, edaphic variables, and management factors as predictor variables. Soil nutrient regime (SNR) exhibited the strongest relationship of all variables examined in the study, explaining 51% of the variation in site index, with site index increasing with increasing SNR. We found that edaphic variables of soil moisture regime and SNR produced the best prediction of site index. The species showed the best development on fresh to very moist sites, with rich to very rich soil nutrient regimes. We also developed a composite model explaining 62% of the variation in site index and an elevation zone model (>300 m) explaining 74% of the variation in site index.
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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 | 0.003 | 0.000 |
| 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) | 0.000 | 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".