Spatial variations of throughfall through secondary succession of evergreen broad‐leaved forests in eastern China
Bibliographic record
Abstract
Abstract Linking spatial variations of throughfall with shifting patterns during forest succession is important for understanding developmental patterns of ecosystem function. However, no such approach has been previously used for the chronosequence of evergreen broad‐leaved forests in subtropical regions. This study was conducted in a chronosequence of secondary forest succession in Tiantong National Forest Park, to determine the optimum number of collectors within certain limits of error. Throughfall was 66, 55 and 77% of gross precipitation in an early‐succession (SS), sub‐climax (SE) and climax (CE) forest, respectively. The coefficient of variations ( CV ) of throughfall reduced with increasing rainfall amounts. Monte Carlo resampling approach was used to find mean values and 90 and 95% confidence intervals of a variable number of collectors ( n ) ranging from 2 to 24. During the study period, with nine collectors at SS, five at SE and five at CE, the error in the mean individual throughfall did not exceed 10%, respectively. This error was reduced to 5% when using 16, 10 and 10 collectors at SS, SE and CE, respectively. The CV s decreased greatly with increasing sample size when the sample size was less than 16 for the three successional stages, regardless of rainfall amounts. Based on the Student's t ‐value analysis of the mean individual throughfall volumes, a sample size of 16 at SS, five at SE and four at CE would be enough for throughfall estimates at an accepted error of 10% of 95% confidence level, respectively. Therefore, we concluded that the 25 of collectors used in the present study were sufficient to estimate the throughfall value at an accepted error of 10% at 90 and 95% confidence levels, even for those small rainfalls in eastern China. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.000 | 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 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".