Gradient analysis, the next generation: towards more plant-relevant explanatory variables
Bibliographic record
Abstract
The long history of gradient analysis is anchored in the observation that species turnover can be described along elevation gradients. This model is unsatisfying in that elevation is not directly relevant to plants and the ubiquitous "elevation gradient" is composed of multiple intertwined environmental factors. We offer an approach to landscape-scale vegetation analysis that disentangles the elevation gradient into its constituent parts through focused field sampling and statistical analysis. We illustrate the approach for an old-growth watershed in the Oregon Western Cascades. Our initial model of this system supports the common observation that forest community types are highly associated with specific elevation bands. By replacing elevation and other crude environmental proxy variables with estimates of more direct and resource gradients (radiation, temperature, and soil moisture), we create a vegetative model with stronger explanatory power than the proxy model in both cross-validation analysis and validation using an independent data set. The resulting model is also more biologically interpretable, which provides more meaningful insight into potential forest response to environmental change (e.g., global climate change scenarios). Acquiring a better mechanistic understanding of the relationship between plant communities and environmental predictor variables presents the next great challenge to community ecologists conducting gradient studies at landscape scales.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".