Adaptive strategies in seedlings of three co-occurring, ecologically distinct northern coniferous tree species across an elevational gradient
Bibliographic record
Abstract
The inherent clinal responses of four quantitative traits thought to be adaptive for trees in cold-limited environments (i.e., height-growth cessation, growth rate, resource allocation to aboveground and belowground tissues, and resource allocation to photosynthetic and nonphotosynthetic tissues in the shoot) were characterized under nonlimiting conditions in a controlled glasshouse study for seedlings of three ecologically distinct and co-occurring northern tree species (Pinus contorta Dougl. var. latifolia Engelm. (lodgepole pine), Picea glauca (Moench) Voss × Picea engelmannii Parry ex Engelm. (interior spruce), and Abies lasiocarpa (Hook.) Nutt. (subalpine fir)). For each species, clinal trends were quantified among populations adapted to increasingly cold-limited climates across an elevation gradient approaching the tree line. In subalpine fir seedlings, strong clinal variation for all the quantitative traits indicated an increasingly conservative response to climate moving toward harsher conditions. Variation in lodgepole pine and interior spruce seedlings suggested a more plastic strategy, favoring competitive traits across a wide range of climate conditions. Study findings suggest that ecologically distinct species may exhibit different strategies in adapting to local climates.
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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) | 0.000 | 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".