Effect of temperature and precipitation on linear increment of Sphagnum fuscum and S. magellanicum in Western Siberia
Bibliographic record
Abstract
The linear increment of Sphagnum fuscum and S. magellanicum in ombrotrophic mires of Western Siberia has been measured during two years over a transect about 2500 km long extending from forest–steppe to forest–tundra. Along the latitudinal gradient, the increment of both species has proved to be correlated with annual average air temperature and, in S. magellanicum , also with annual precipitation. The determinants of their growth differ between the southern, central, and northern parts of the study region. At the regional level, the annual and summer precipitation plays a more important role than the average air temperature. The increment of S. fuscum in the southern part is positively correlated with the amount of precipitation and negatively correlated with summer temperature, whereas the situation in the central part is inverse. In S. magellanicum , the linear increment is directly dependent on the annual average temperature and annual and summer precipitation in the south and on the annual and summer precipitation in the north of Western Siberia. The dynamics of linear growth of both species in bog pine forests during the growing season are similar: its rate is the highest in June, when the linear increment of S. fuscum and S. magellanicum amounts to 60 and 85% of the annual total, respectively.
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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".