Environmental Drivers of Succession in Jack Pine Stands of Boreal Ontario: An Application of Survival Analysis
Bibliographic record
Abstract
In this study, we present a quantitative approach to examining species compositional changes in jack pine (Pinus banksiana Lamb.)-dominated forests that combines photo chronosequencing and survival analysis. Study sites (178) were selected from Ontario’s Growth and Yield Permanent Sample Plot network and supplemented with archived aerial photographs that captured stand conditions at four additional points in time. Environmental attributes specific to geographic location, topography, soil characteristics, and climate were also used in the analysis. The nonparametric Kaplan-Meier method was used to derive cumulative survival functions, and Cox regression analysis was used to determine the significant environmental factors that resulted in downward shifts in jack pine persistence over time. Only 26% of the stands included in this study were observed to have a pure jack pine canopy during some stage of stand development, with black spruce (Picea mariana [Mill.] B.S.P.) being the most common associate. Although shifts in species composition occurred in the majority of stands, much of the observed succession was a reflection of differential growth rates and responses to suppression between contemporaneously established populations. Based on the Cox regression model, sites with sloped terrain, sites that had deep sandy soils, and sites that received high precipitation during the growing season all retained high abundances of jack pine over time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".