Relative competitiveness of nine early-successional boreal forest species associated with planted jack pine and black spruce seedlings
Bibliographic record
Abstract
Differences in yield-density models derived from an additive experimental design were used to compare the relative competitiveness of nine early-successional boreal forest plants (aster, grass, fireweed, fern, raspberry, willow, alder, birch, and aspen) on jack pine (Pinus banksiana Lamb.) and black spruce (Picea mariana (Mill.) BSP). A randomized complete block split-split-plot design with three replications blocked on soil type was used. Initial density gradients were 0-4 plants/m 2 for woody and 0-8 plants/m 2 for herbaceous species. An a priori analytical approach that compared a full model (using linear regression analysis of 4th-year stem diameter of conifers under increasing cover and height of competitors) to various reduced models was used to assess competition. Increasing cover and (or) height of all competitors (except fern) significantly (P < 0.05) decreased conifer stem diameter. The final regression model (based on visual estimates of cover and differences in initial conifer size) accounted for 89% of the variation in stem diameter. During the years studied, both conifers responded similarly to competition, and herbaceous species were on average 28.9% more competitive than woody species. Under different growing conditions (e.g., a natural forest) the relative competitiveness of herbaceous and woody species may vary from these results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".