Calcium and Magnesium Dynamics in Litter in a Successional Forest Ecosystem, Under Hydroperiod
Bibliographic record
Abstract
This study was part of the Manipulation of Moisture and Nutrient Availability in Young Regrowth Forests in Eastern Amazonia Project (MANFLORA). The experiment was designed in completely randomized blocks containing control and irrigated treatments during the dry period (5 mm of water/day), with four repetitions each. The monthly mean litter values ranged from 316.10 to 997.90 kg ha-1 month-1. The magnitude of this phenomenon can be explained by the functional role of the floristic structure, represented by the species Myrcia sylvatica (G. mey) DC., Myrcia bracteata (Rich) DC., Miconia ciliata (Rich) DC., Lacistema pubescens Mart., Lacistema aggregatum (Berg.) Rusby, Vismia guianensis (Aubl.) Choisy, Cupania scrobiculata Rich. and Ocotea guianensis Aubl., which constituted the determinant factors, associated with the hydroperiodic effect and ecosystem manipulation. The monthly mean of the analytical results of mass treatments were significant (P < 0.05), however, when compared annually there was no significance, which indicates seasonal influence, since the period of greatest deposition is the dry one, regardless of the water manipulation along the period studied. Only in time the mass values of Ca and Mg were not significant for treatment (P < 0.05). The amount of Ca was significantly (P < 0.05) higher than that of Mg.
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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 | 0.000 | 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 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".