Nipa (Nypa fruticans Wurmb) Sap Collection in Southern Thailand II. Biomass and Soil Properties
Bibliographic record
Abstract
Nipa (Nypa fruticans Wurmb) frond biomass is the cruicial factor for sap/sugar production. An allometric equation was firstly formulated for estimating frond biomass from the length (L) and the diameter at breast height (DBH) of a frond. And subsequently the L-DBH relationship was applied for L estimation at five nipa farms. As a result, the allometric equation was expressed as Log DW = 0.85 x Log D2L + 1.54 (r2 = 0.94). The highest above-ground biomass was 83 t/ha, whereas the average biomass in other farms was 32 t/ha, which was almost the same as that in young oil palm plantation. The physical and chemical properties of soils in the nipa farms were analyzed using 80 soil samples to elucidate the factors determining nipa soil properties by principle component analysis (PCA). Consequently, organic matter, salt content and brackish water were considered to be the key factors determining nipa soil properties. Daily sap production (ml/day) (DSP) was well explained by frond biomass and above-ground biomass with the equations: DSP = 17.6 x frond biomass (kg) + 256.1 (r2 = 0.48) and DSP = 11.4 x above-ground biomass (t/ha) + 263.4 (r2 = 0.57), respectively. When soil properties are included, sap production is better explained, as shown by multivariate analysis followed by PCA with the equation, DSP = 609.7 – 92.9 x PC1 – 58.5 x PC2 (r2 = 0.93). As PC1 and PC2 correspond to the organic matter factor and the salt content factor, sap production would be controlled by the nipa growth and the amount of Na in soil.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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 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".