Diversity Relationships among Taxonomic Groups in Recovering and Restored Forests
Bibliographic record
Abstract
Abstract: Our objective was to reexamine the definition and use of surrogates in biodiversity studies of disturbed ecological communities. To this end, we examined diversity and community structure in recovering (pollution damaged) and restored (via liming, fertilizing, seeding, and planting) forests in the Great Lakes‐St. Lawrence zone near Sudbury, Ontario, Canada. The relationships among taxonomic groups were determined using correlations between Shannon diversity and species richness. We used correspondence analysis to quantify the contribution of taxonomic groups to diversity and community structure. We detected useful surrogates in the naturally recovering forests but not in restored forests. In the former, vascular plant diversity was significantly correlated with nonvascular plant diversity and reflected community structure in the total plant community. Our results suggest that it may be important to restore and conserve diversity relationships rather than simply diversity levels because the relationships may be better indicators of ecosystem health or function.
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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".