Stable Isotopes in Tree Rings of Boreal Forests
Bibliographic record
Abstract
Abstract The boreal forests are widely expanded from subarctic forest to tundra, and from taigato forest-steppe zone (from 50 °N to 70 °N). We reviewed available stable isotope chronologies in tree-ring cellulose (δ13C, δ18O and δ2H) from 16 sites located in the Russian Federation; 4 research sites from Fennoscandia (Finland, Sweden and Norway); 5 sites from Canada, and 1 site from Alaska (USA) to evaluate impact of climatic changes from seasonal to annual scale across boreal forest ecosystems. Results of our review of carbon isotope data showed that droughtconditions (mainly high vapour pressure deficit) are prevalent for western and central regions of Eurasia, Alaska and Canada, while northeastern and eastern sites of Eurasian subarctic are showing water shortage developments resulting from decreasing precipitation. Oxygen isotopechronologies show increasing trends towards the end of the twentieth century mainly for all chronologies, except for the Siberian northern and southern sites. The application of the multiple stable isotope proxies (δ13C, δ18O, δ2H) is beneficial to study responses of boreal forests to climate change in temperature-limited environments. However, a deeper knowledge of hydrogen isotope fractionation processes at the tree-ring cellulose level is needed for a sound interpretation and application of δ2H for climate reconstructions, especially for the boreal forest zone where forest ecosystems are more sensitive to climatic and environmental changes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".