Typhoon effects on litterfall in a subtropical forest
Bibliographic record
Abstract
The litterfall in a subtropical broadleaf forest within the Fushan Experimental Forest in northeastern Taiwan was monitored for 9 years. Mean annual litterfall was very sensitive to typhoon frequency and intensity, ranging from 3 to 11 Mg·ha 1 ·year 1 . Litterfall was significantly higher in years with strong typhoons than in years without typhoons, and the number of strong typhoons explained 82% of interannual variation in litterfall. Nutrient-use efficiency (dry mass/nutrients in litterfall) was high for N, but low for P compared with other tropical forests. This result supports the idea that the study forest is P limited but not N limited. Nutrient loss via litterfall represents a large percentage of aboveground biomass, especially during years with strong typhoons (e.g., 19%41%, 15%40%, 5%12%, for N, P, and K, respectively). Forests that experience infrequent wind disturbance (e.g., temperate or boreal forests) can gradually regain any lost nutrients prior to the next disturbance; this is different from the situation observed in the Fushan Experimental Forest. At Fu-shan the pattern of not responding to typhoons with a flush of new growth appears to be an adaptation to the frequency with which there are multiple typhoons affecting the forest in a single year. Nutrient loss in litterfall caused by frequent typhoon disturbances appears to limit tree growth and contributes to the very low canopy height of the Fushan Experimental Forest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".