Root and shoot phenology and root longevity of Norway spruce saplings grown at different soil temperatures
Bibliographic record
Abstract
Tree roots comprise a huge carbon pool. Their dynamics are driven by environmental factors and thereby affected by climate change. We studied the effects of soil temperature on root and shoot phenology and their linkages in Norway spruce (Picea abies (L.) Karst.). Saplings were grown in controlled-environment rooms for three simulated growing seasons (GS1, GS2, and GS3). Soil-temperature treatments of 9, 13, 18, and 21 °C were applied during GS2. Root growth was monitored with minirhizotrons and commenced in all treatments simultaneously. Temporal growth patterns of short and long roots were usually bimodal. Root growth was very low during the coldest treatment of GS2 but increased during GS3 as an aftereffect. During GS3, growth of short roots continued later after colder treatments than warmer treatments. Reduced sink strength of roots and increased carbohydrate accumulation into needles at 9 °C during GS2 probably enabled compensatory root growth under restored temperatures during GS3. Soil temperature did not affect shoot phenology, and root and shoot phenology varied between growing seasons; thus, the linkage of root and shoot phenology was inconsistent. In warmer soil, root longevity was shorter and turnover rate was higher than in colder soil. This can further affect soil carbon dynamics and ecosystem carbon cycling in boreal forest ecosystems.
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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".