Comparing power among three sampling methods for monitoring forest vegetation
Bibliographic record
Abstract
We compared three methods of sampling forest vegetation for their ability to reliably estimate changes in species richness, plant abundance, and overstory basal area and composition. Methods include the US Forest Service’s Forest Inventory and Analysis (FIA) method and two other methods being considered for use in monitoring National Parks in the Northern Great Lakes ecoregion. All methods were successful at detecting changes in composite variables but lacked sufficient enough power to detect a 20% change in the abundance of most individual species. All three methods had high power for detecting changes in overstory tree communities but differed greatly in their ability to track shifts in understory composition and diversity. Although complete walk-through surveys of all species present provided adequate power for tracking changes in diversity, sampling only 12 ground layer quadrats limited the power of the FIA method. Methods that sample the understory more intensively provide a better balance of sampling effort and provide higher power to detect changes in forest understory communities. Aggregating data across sites of similar habitat also provides more powerful estimates of change.
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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 | 0.116 | 0.271 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".