Light and temperature differentially colimit subalpine fir and Engelmann spruce seedling growth in partial-cut subalpine forests
Bibliographic record
Abstract
We compared the relative impacts of light, soil N, and soil temperature on Engelmann spruce (Picea engelman nii Parry ex Engelm.) and subalpine fir (Abies lasiocarpa (Hook.) Nutt.) seedling growth by quantifying (i) microsite environment-growth relationships for N-fertilized and unfertilized planted seedlings in shelterwood, patch cut, and clearcut harvest treatments and (ii) growth, photosynthesis, and biomass allocation for greenhouse-grown seedlings at 5, 10, and 15 °C soil temperatures. Fertilization did not affect seedling growth. Furthermore, soil N availability did not vary among harvest treatments. In contrast, clearcut compared with shelterwood seedlings had greater mass (fivefold), light availability (twofold), and soil temperatures (1.6 °C). Across harvest treatments, spruce and fir mass increased linearly to 100% open-sky light (R2 = 0.51 and 0.57, respectively), and temperature and light combined explained more variation in mass than light alone (adjusted R2 = 0.58 for both species). Spruce growth was more sensitive to temperature than fir in both field and greenhouse experiments. Diminished growth at low soil temperature was associated with lower photosynthesis and not lower leaf fraction. Thus, soil temperature and light colimit seedling growth in subalpine forests, but responses were species-specific and consistent with microenvironment differences in spruce and fir regeneration niches.
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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".