Spatiotemporal changes in radial growth and response to climate for Picea abies along environmental gradients
Bibliographic record
Abstract
This thesis deals with effects of climate on tree growth of the dominating conifer species, Picea abies (L) Karst. (Norway spruce) and Pinus sylvestris L (Scots pine), in central Norway and Fennoscandia. Both species are sampled along major environmental gradients, i.e. altitude and oceanicity, and growth responses to climate, i.e. temperature and precipitation, are examined along these gradients. Additionally, time is considered as an environmental gradient and temporal responses are carefully deciphered. Special attention is given to large-scale climate oscillation and their effect on tree growth. In the individual papers the specific aims have been to:1. identify climate variables (all seasons) with significant influence on radial tree growth of P. abies and P. sylvestris along major environmental gradients (Paper I-IV)2. identify if and how the growth response to climate has changed through time along these gradients (Paper I-IV)3. make interregional comparisons of P. sylvestris growth pattern across Fennoscandia from oceanic western Norway to continental eastern Finland (Paper III)4. analyse to what degree large-scale circulation patterns of air masses are registered in regional tree growth of both P. abies and P. sylvestris (Paper I, III and IV)5. discuss possible effects on radial tree growth of a predicted warmer climate (Paper I, III and IV)
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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".