Determination of Soil-Related Factors Controlling Initial Nipa (Nypa fruticans Wurmb.) Growth in an Abandoned Shrimp Pond
Bibliographic record
Abstract
<p class="1Body">In an abandoned shrimp pond, the spatial variability of the height of nipa (<em>Nypa fruticans</em> Wurmb.) plants 5 years after their planting was evaluated to elucidate the determining factors for height. Soil properties were evaluated in 20 points in an area of 0.48 ha. Physical (e.g., hardness, water content) and chemical (e.g., pH and EC) properties were determined using surface soil samples collected at each point. Nipa height was moderately variable, showing a CV value of 31.2%. Soil conditions were considered to affect nipa height; therefore, principal component analysis (PCA) was conducted to elucidate the relationships among soil properties. As a result, three factors were extracted. PC1 correlated positively with relative ground level and Eh, and negatively with moisture. Because these properties were closely related to topography, PC1 was referred to as the topography factor. PC3 showed the second important contribution, a high K content contributed to a large nipa height. Multivariate analysis was also performed to examine the spatial property of nipa height (H), and as a result, the following equation was obtained: H = 423.94 - 0.39 x PC1 - 0.57 x PC3 (r<sup>2</sup>=0.53). Results of this study indicate that 53% of nipa height variation was explained by soil properties, which is larger than the nipa height variation explained solely by topography (23%). Site-specific management to regulate soil properties could be a practical strategy for nipa plantation.</p>
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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 | 0.000 | 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".