Seasonal and annual dynamics of litterfall
Bibliographic record
Abstract
Long-term data of litterfall can indicate overall forest functions in forest ecosystems. We collected monthly (May – October) and annual (1981 – 2018) litterfall including leaves, twigs, bark, reproductive and miscellaneous fractions in a mixed mature Pinus koraiensis forest on Changbai Mountain in Northeast, China, across 30 years. Based on these long-term litterfall data, we analyzed the seasonal and annual variations in different litterfall fractions and their relationships with climatic factors. Climate data were obtained from the meteorological observation field (738 m above sea level) of the Changbai Mountain forest ecosystems research station, Institute of Applied Ecology, Chinese of Academy of Sciences. Both the leaf and total litterfall exhibited a strong, similar seasonal pattern, with the highest levels between September and October, and the annual litterfall had an “S-shaped” increasing pattern from 1981 – 2018. Distinct monthly and yearly fluctuations for the other litterfall fractions across the 30 years were observed. Mean monthly evapotranspiration and temperature (minimum and maximum) were the best predictors for monthly litterfall. By contrast, the models that best predicted the annual litterfall production included mean annual precipitation, and mean monthly precipitation and temperature in May and October. This unique dataset of detailed long-term litterfall dynamics has potentially major significance for enhancing our understanding on the role of climatic factors controlling forest litterfall amount and seasonality in temperate mixed mature forests.
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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 | 0.001 | 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) | 0.001 | 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".