Determination of optimal riparian forest buffer dimensions for stream biotalandscape association models using multimetric and multivariate responses
Bibliographic record
Abstract
The dimensions of riparian buffers selected for stream biotalandscape association models determine correlation strength and subsequent model interpretation. Efforts have been made to optimize buffer dimensions incorporated into models, but none has explicitly determined a single optimum based on both longitudinal and lateral buffer dimensions. We applied partial correlation and multivariate linear regression on functional fish community response attributes and the index of biotic integrity using stream samples (N = 107) from the Eastern Corn Belt Plain Ecoregion of Indiana, USA. Land-cover data in digital format were processed in geographic information systems for an area covering 300 m on either side of selected streams and within 2000 m longitudinally. The optimal buffer dimension for the study area was 30 m laterally and 600 m longitudinally, with a partial correlation of 0.29 (P = 0.002), and there was agreement in the partial correlation and multiple regression models. The longitudinal dimension was more conclusively determined, but the lateral dimension was optimum only with respect to the resolution of the land-use data used. Based on these results, we propose the use of this approach to optimize the riparian buffer parameter in landscape models.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".