Effects of Soil Type on Floristics and Stand Structure in Amazon Unflooded Forests
Bibliographic record
Abstract
The importance of unflooded forests in the Amazon and the need to investigate how their soils affect their floristics and stand structure, lead me to set up and sampled trees in four plots in terra firme forest, in white sand forest and in palm forest at the same site in the Peruvian Amazon. I found (1) the white sand forest plot had 15 families with Clusiaceae, Malvaceae and Myrtaceae the most common and Pachira brevipes the most common species, (2) the palm forest plot had 6 families with Arecaceae, Clusiaceae and Fabaceae the most common and Socratea exorrhiza the most common species, and (3) the terra firme forest plot had 47 families with Myristicacea, Fabaceae and Bombacace the most common and Otoba parvifolia and Astrocaryum murumura the most common species. For the stand structure of these forests, (1) terra firme had more stems and larger stems than white sand and palm, and while palm had more stems than white sand, white sand had larger stems than palm, (2) species richness was greatest in terra firme and decreased by a factor of three in white sand, and by a factor of five in palm, (3) basal area decreased from terra firme to white sand, but white sand and palm were comparable, (4) white sand had the greatest above-ground biomass, followed by terra firme and palm, and (5) white sand and palm were much more open forests than terra firme. Results strongly suggest that soils are a significant causal factor in determining floristic and structural differences among these Amazon forests, where the richer soil of terra firme forest helps produce both more structure and a richer floristics compared to the poor soil of palm forests and the even poorer soil of white sand forests.
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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 | 0.000 | 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 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".