An approach to illustrate the naturalness of the Brazilian Araucaria forest
Bibliographic record
Abstract
The concept of “naturalness” can be associated with conservation status, resilience, and biodiversity. Its most common definition relates to the degree to which a resource is similar to its original state. Hence, we developed a naturalness assessment method for the Brazilian Araucaria forest. We used data collected within 145 systematically distributed plots over an area of ∼56 000 km2. We selected five indicators to compose a unified naturalness index: (i) evidence of human activities inside the forest stand; (ii) abundance of naturalness-indicator species; (iii) standard deviation of diameter at breast height (Sdbh); (iv) species diversity of the understory–natural regeneration layer; and (v) forest stand landscape metrics. We then calculated the Euclidean distance between the vector generated from the indicators of an ordinary forest stand and the vector generated from a theoretical reference forest (TRF) with maximum naturalness. The reduced Sdbh reflected the stands’ diminished structural diversity as result of historical logging and other ongoing human activities. Most stands presented average naturalness compared with the TRF. Besides the lack of data on undisturbed forests to thoroughly evaluate the naturalness index, evidence suggested that it summarized relevant forest attributes to the extent that protected areas presented greater naturalness than nonprotected areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".