Local testing and calibration of species-specific competition indices in Sierran mixed-conifer forests: application transfer to evolving objectives
Bibliographic record
Abstract
Forest growth processes are driven by site productivity and species functional traits, which are ultimately constrained by cumulative resource demand, resulting in competitive dynamics across successional forest communities. Historical efforts to quantify competition used density metrics or neighborhood crowding indices for yield modeling and reforestation surveys. These methods have expanded to include dendroclimatology and restoration applications that commonly assume similar competitive response across species of various functional types. We assessed the competitive indices of two focal species, Pinus lambertiana (Dougl.) and Pinus ponderosa (Dougl. ex P. Laws. & C. Laws.) in mixed-conifer forests of the Sierra Nevada to estimate stem radial growth under current stand structure. We ranked correlations of basal area increment of the last 10 years (BAI10) separately across 20 competition indices (CIs). The best-ranked CIs were used to test the relative influence of competition, tree size, and site variables on BAI10 with linear mixed models. While crown overlap was a common variable in CIs among both species, BAI10 of P. lambertiana was less impacted by intraspecific competition, and P. ponderosa appeared sensitive to all competing stems. The results suggest that local calibration of CIs with crown parameters may aid in interpreting Pinus species growth patterns and that the relative impact of competition on growth is species specific.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".