Leaf litter dynamics in Western Black Sea mountainous forest ecosystems
Bibliographic record
Abstract
This study aimed to estimate leaf litter decomposition rates in eastern beech (Fagus orientalis Lipsky) and sweet chestnut (Castanea sativa Mill.) mixed stands in Akçakoca in the province of Düzce, located in the Western Black Sea region of Turkey. The sampling areas represent four different elevations and two aspects at each elevation. Amounts of annual beech and chestnut litterfall were estimated at 5.19 and 4.61 Mg·ha−1, respectively. Litter decomposition was examined over five time periods (0.25, 0.50, 1.25, 2.25, and 4.25 years) by using the litterbag method. The mass of remaining beech leaf litter was found to be 1.1, 1.2, 1.2, 1.4, and 1.3 times greater than the mass of chestnut leaf litter, respectively. However, estimated values for the decomposition rate constant (k) of chestnut for all time periods were found to be approximately 1.5 times greater than those of beech leaf litter. Litter in beech stands decomposed more rapidly at higher elevations during the first year but at lower elevations during the second year, likely due to increased temperature and precipitation for the corresponding years. Leaf litter in chestnut stands decomposed more rapidly at lower elevations in the second and fourth years, reflecting higher precipitation for those years.
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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 | 0.000 |
| 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) | 0.000 | 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".