NEIGHBOUR`S IDENTITY OF COMMERCIAL TROPICAL TREE SPECIES IN A TROPICAL RAINFOREST NEAR MANAUS, BRAZIL
Bibliographic record
Abstract
The use of spatially explicit neighbourhood approach helps to understand the processes which structure and guide tree communities over space and time, contributing for the conservation and forest management. We investigated the neighbours of Brosimum spp., Eschweilera coriacea, Ocotea cernua and Protium hebetatum, hypothesizing that there is a taxonomic pattern around these focal species, been important information for the maintenance of the forest’s structure submitted to the management actions. We used a 2-ha plot in a tropical rainforest in Brazil where all trees with diameter at breast height ≥ 10.0 cm were stem-mapped in 2005. First, we determined how focal species were spatially structured by using Ripley’s K function. For the neighbourhood analysis, the nearest 20 trees around focal trees were identified to compute the mean richness, mean proportion of conspecifics, relative frequency distribution and the number of neighbour species by distance from focal trees. Our findings demonstrate that conspecific neighbours are occurring associated with focal trees, mainly at shorter distances for all focal species with possible more intra-specific interactions as a very few heterospecific neighbours were associated with focal trees. The spatial structure, more than abundance of focal species, may have contributed for the conspecific encounters, mainly for Brosimum spp. and Ocotea cernua. Rare species were found frequently associated with focal species, calling our attention for the effects of the forest management of commercial trees on community structure in order to prevent local extinctions.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".