Flood disturbance and shade stress shape the population structure of açaí palm <i>Euterpe precatoria</i>, the most abundant Amazon species
Bibliographic record
Abstract
Euterpe precatoria Mart. is the most abundant plant species in the Amazon basin, and one of the main non-timber forest products on the continent. A thorough understanding of the ecology of this species is needed to support sustainable management initiatives. Resource availability, disturbance regime, and human management are some of the main factors influencing population structure. We described the species’ life stages, evaluated its allometric relationships, and assessed the effects of habitat type (floodplain and upland) and proximity to human settlements on population size distribution in the Central Amazon near the Purus River. The height:diameter ratio increased from Seedlings to Juvenile 2, but decreased from Juvenile 2 to Reproductive 2, indicating changing height investment for any given diameter along these life stages. There was a marked habitat dependency in both the density and population size distribution, with populations in upland forests dominated by juveniles, whereas populations in the floodplains were dominated by reproductive palms. Proximity to human settlements was not related to population structure parameters. Our results suggest that the disturbance regime may have opposite meanings in várzea forests, where it limits recruitment under increased light levels, and in terra firme forests, where it may stimulate recruitment under limited light conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 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".