Critical period of interspecific competition for four northern conifers: 10-year growth response and associated vegetation dynamics
Bibliographic record
Abstract
The influence of the timing and duration of interspecific competition on planted jack pine (Pinus banksiana Lamb.), red pine (Pinus resinosa Ait.), eastern white pine (Pinus strobus L.), and black spruce (Picea mariana (Mill.) BSP) was assessed using 10-year growth responses in a northern Ontario experiment. Stand volume was 117%, 208%, 224%, and 343% higher for jack pine, red pine, white pine, and black spruce, respectively, with 5 years of vegetation control than with no vegetation control. Stand volume increased linearly with number of years of vegetation control, and the slope of the relationship varied among conifer species. Change-point regression analysis was used to derive segmented weed-free and weed-infested curves, and to simultaneously estimate key critical-period parameters. Weed-free and weed-infested curves in the 10th year were similar to those derived in year 5, indicating that the patterns established during the first few years after planting were relatively robust for the first decade. The critical-period was 2 and 3 years after planting for jack pine and red pine, respectively, and occupied most of the 5-year period for white pine and black spruce. Principal components analysis of the vegetation community indicated that repeated herbicide applications caused differential shifts in the relative abundance of shrub, fern, and moss species through the 10th year. Species richness, however, was not substantially different between the untreated control and the most intensive treatments. Difference modeling was used to quantify how annual volume increment during the first decade varied with time, conifer species, cover of woody and herbaceous vegetation, and stage of development.
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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 | 0.000 | 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 | 0.000 |
| 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".