Nutrient distribution in Picea likiangensis trees growing in a plantation in West Sichuan, Southwest China
Bibliographic record
Abstract
We measured nutrient distribution of Picea likiangensis (Franchet) E. Pritzel var. balfouriana trees growing in a plantation by field investigations, sample tree and plot harvest in West Sichuan, Southwest China. Based on the results in this study, the total biomass of plant compartments in plantation ecosystem was 114 829.1 kg ha–1. Tree, shrub, herb, bryophyte and litter layers accounted for 93.9%, 0.9%, 0.02%, 0.04%, 5.2%, respectively. The total biomass of tree layers was 107 817.1 kg ha–1. Needles, branches, stem wood, stem bark and roots accounted for 13.2%, 19.7%, 42.3%, 10.0% and 14.8%, respectively. The concentration of the nutrients was generally highest in the actively growing parts of the trees (e.g. needles) and lowest in the structural and not actively growing parts (e.g. stem wood). On the other hand, the concentrations of N, P, K and Mg were generally higher in the current year needles and branches than in the older needles and branches. These nutrient concentrations were also higher in the upper stem wood and bark than in the lower stem wood and bark, and in small roots than in large roots, whereas the opposite patterns were observed for the concentration of Ca in these compartments. The results will be helpful in understanding the nutrient behavior in a highly productive forest plantation and thereby providing decisive information for their sustainable management.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".