Development and evaluation of a biomass increment based index for site productivity
Bibliographic record
Abstract
Measures of forest productivity generally rely on site index, which can be problematic for multicohort and mixed-species stands. Using stand growth and dominant tree height–age (i.e., site tree) measurements from ∼10 900 plot locations from Maine, Nova Scotia, New Brunswick, and Prince Edward Island, a forest productivity model for the Acadian Forest Region was developed as a function of climate, lithology, soils, and topographic metrics. Approximately 65% of variation in observed aboveground dry-biomass growth rate (BG) was explained by a Chapman–Richards function of temperature, bedrock, soil root space, slope, and depth to water in combination with stand structure and species predictors. Productivity was then defined in terms of the predicted asymptote of BG, holding structure and species constant, which was termed biomass growth index (BGI), i.e., the site-influenced component of the BG relationship. BGI was mapped on a 20 m grid throughout the region. BGI explained 0%–30% of the variability in spruce (Picea sp.) and balsam fir (Abies balsamea (L.) Mill.) site index and had similar site index predictive performance (±5%) when compared with existing land productivity classifications in each province. BGI provides a direct relationship between site variables and growth and can help guide forest management decisions and future research.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".