Chemical content and seasonal variation of throughfall and litterflow under individual trees in the Hyrcanian forests of Iran
Bibliographic record
Abstract
We assessed the effect of seasons and the tree species velvet maple (Acer velutinum Boiss), Hainbuche hornbeam (Carpinus betulus L.) and chestnut-leaved oak (Quercus castanefolia C. A. Mey.; Fagaceae) on nutrient fluxes (Na+, K+ and Ca2+) in throughfall (TF) and litter flow (LF) from January 2012 to February 2013. Potassium (K+) and calcium (Ca2+) in total TF and LF over the measurement period significantly differed between 3 the species (p < 0.05). Maple and oak trees had significantly higher fluxes of K+ and Ca2+ in their TF, respectively. The average quantity of nutrients in LF was significantly higher in the leafy season than in the leafless season for all cations except for Na+. Under velvet maple trees, the average flux of nutrients (Na+, K+, and Ca2+) in TF was significantly higher than LF. Lastly, leaching of potassium (K+) and calcium (Ca2+) in LF during the measurement period was significantly lower than TF under the hornbeam trees. Our results suggest that different tree species may impact the nutrient availability and possibly the sustainability of Hyrcanian forests. Further research is needed to determine how managing different species will affect the long-term nutrient status of these 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) | 0.000 | 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".