Macronutrient input from pollen in two regenerating pine stands in southeast Korea
Bibliographic record
Abstract
This study examined macronutrient input from pollen in two naturally regenerating pine stands in southeast Korea. Durham gravity pollen collectors were used to measure pine pollen deposition and the macronutrients in the collected pine pollen were analyzed. In 1998, pine pollen deposition began just before 18 April and lasted for approximately 2 weeks. Total pine pollen deposition differed between the two sampling sites; 27.5 kg ha −1 was collected from the mature stand and 17.7 kg ha −1 was collected from the young stand. The values for nutrient deposition from pine pollen are 549 g ha −1 N, 78 g ha −1 P, 240 g ha −1 K, 45 g ha −1 S and 22 g ha −1 Mg at the mature stand and 353 g ha −1 N, 51 g ha −1 P, 151 g ha −1 K, 27 g ha −1 S and 14 g ha −1 Mg at the young stand, suggesting that nutrients from pine pollen contribute to forest nutrient cycling. The pine pollen deposition values obtained from our study (17.7–27.5 kg −1 ha −1 year −1 ) are approximately 1/115–180‐fold that of pine litterfall in Korea. If we take pollen nutrients into account, the contribution rate of pollen to the annual nutrient input is very high in our study (N 1/30, P 1/5, K 1/9 that of litterfall). Macronutrient deposition from pine pollen is concentrated temporally in spring. Although the annual contribution of nutrient mass by pollen is small compared to that of litterfall, the rapid turnover rate of pollen nutrients combined with episodic deposition suggests that pollen may play a disproportionate role in temperate pine forest nutrient cycling.
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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".