Evaluating and modeling variation in site-level maximum carrying capacity of mixed-species forest stands in the Acadian Region of northeastern North America
Bibliographic record
Abstract
Currently no universal approach exists to estimate regional site-level maximum carrying capacity in terms of stand densityindex (SDIMAX) of mixed species stands across contrasting forest ecosystems. Regional research efforts that account forinfluential stand-level variables and species traits are needed to reliably derive SDIMAXunder varying environmental conditions and stand characteristics. This study used regionally comprehensive forest inventory data from various permanentsampling efforts to evaluate the effects of contrasting biotic and abiotic stand- and site-level factors on SDIMAXof multiple-species, structurally heterogeneous stands of the climatically diverse Acadian Forest Region of North America. Specifically,we aimed to i) quantify the stand-level maximum size-density line for an array of forest stands found across the study area,irrespective of stand structure; ii) evaluate the relationship between this stand-specific estimate of SDIMAXand various other stand-level attributes; and, iii) develop a generalized SDIMAXprediction model using SDIMAXestimates from objective i) aswell as potential regional drivers of SDIMAXfrom objective ii). The most influential stand-level factors on SDIMAXwere proportion of total stand basal area in hardwood species, basal area weighted mean specific gravity, range in stem diameter, andspecies diversity. Direct climatic variables were not included in our SDIMAXprediction model due to the limited variationexplained, but relationships with elevation and a site quality index based on these climatic variables were. Overall, we con-clude that i) variation in SDIMAX appears to be mostly driven by the softwood to hardwood ratio of the mixed species,structurally complex stands evaluated in our study and ii) the general approach offers a viable framework for estimating sitemaximum carrying capacity at a regional-scale and effectively managing stand density accordingly.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 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 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".