Multi-scale analysis on species diversity within a 40-ha old-growth temperate forest
Bibliographic record
Abstract
forest were applied to the Individual Species-Area Relationship model (ISAR) to determine distribution patterns for species richness. The ecological processes influencing species abundance distribution patterns were assessed by applying the same data set to five models: a Log-Normal Model (LNM), a Broken Stick Model (BSM), a Zipf Model (ZM), a Niche Preemption Model (NPM), and a Neutral Model (NM). Each of the five models was used at six different sampling scales (10 m × 10 m, 20 m × 20 m, 40 m × 40 m, 60 m × 60 m, 80 m × 80 m, and 100 m × 100 m). Model outputs showed that: (1) Accumulators and neutral species strongly influenced species diversity, but the relative importance of the two types of species varied across spatial scales. (2) Distribution patterns of species abundance were best explained by the NPM at small scales (10 m-20 m), whereas the NM was the best fit model at large spatial scales. (3) Species richness and abundance distribution patterns appeared to be driven by similar ecological processes. At small scales, the niche theory could be applied to describe species richness and abundance, while at larger scales the neutral theory was more applicable.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".