Water input from fog drip in the tropical seasonal rain forest of Xishuangbanna, South-West China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fog drip and related microclimatic factors were measured between January 1999 and December 2002 at a tropical seasonal rain forest in Xishuangbanna, South-West China. During the study period, the annual average fog drip was 89.4±13.5 mm (mean±1 SD). Fog drip contributes an estimated 5% of the annual rainfall, with 86% of the fog drip occurring in the dry season (November–April). Annual fog drip was negatively correlated with annual rainfall. Monthly variation in fog drip was also negatively correlated with monthly rainfall. Average daily fog drip was 0.38±0.27 mm d −1 for all days on which fog drip occurred. Daily fog drip was negatively correlated with minimum air temperature and positively correlated with mean above-canopy wind speed. The results indicate that fog drip is an important additional input of water to this seasonal rain forest during the dry season.
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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) | 0.000 | 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 it