Assessing Soil-Related Black Spruce and White Spruce Plantation Productivity
Why this work is in the frame
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Bibliographic record
Abstract
This article focuses on modelling and mapping the productivity of black (Picea mariana) and white spruce (Picea glauca) plantations across the Black Brook forest management area in northwestern New Brunswick, Canada, encompassing about 200,000 ha. This effort involved establishing 3500 50 m2 survey plots, each informing about: plantation age (15 to 43 years), planted species type, stem count, tree height, basal area, and wood volume. All of this was supplemented with location-specific productivity predictors, i.e., xy location and specifications pertaining to soil type, soil drainage (established through digital elevation modelling by way of the depth-to-water index DTW), and years since thinning (pre-commercial and commercial), and. The DTW index, as it emulates the elevation rise away from open water features such as streams, rivers and lakes, allowed the re-mapping of existing soil borders by topographic position and drainage association. Non-linear regression analysis revealed that plantation height, basal area and volume all increased with plantation age, as to be expected. Pre-commercial thinning in plantations <30 years old had a positive while the more recent commercial thinning still had the negative effect on standing wood volume and mean annual volume increment (MAI). White spruce MAI generally exceeded black spruce (MAI) by a factor of 1.25. Poor and excessive soil drainage reduced MAI. Best growth performances occurred on plantations established on well-drained calcareous soils. The best-fitted results so obtained allowed for generating black and white spruce MAI maps across the forest management area by ridge-to-valley soil and DTW location at 10 m resolution. These maps were subsequently used for site-by-site silvicultural evaluation and ranking purposes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 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 it