Statistical Methods for Ecological Breakpoints and Prediction Intervals
Bibliographic record
Abstract
The relationships among ecological variables are usually obtained by fitting statistical models that go through the conditional means of the variables. For example, the nonparametric loess regression model and the parametric piecewise linear regression model - that go through the conditional mean of the response variable given the predictor - are used to analyze simple to complex relationships among variables. In this article, we have proposed to use loess to identify the number and positions of ecological breakpoints, and the piecewise linear regression model to estimate the breakpoints. In contrast, the piecewise linear quantile regression model - which goes through the quantiles of the conditional distribution of the response variable given the predictor - provides much richer information in terms of estimating relationships and breakpoints. We have proposed to use the piecewise linear quantile regression to estimate the breakpoints and thus to obtain prediction intervals of the breakpoints. Three statistical methods have been proposed to construct confidence bands for the ecological response given the human induced disturbances to the nature. The methods are illustrated with two examples from the ecological literature - relating an index of wetlands' fish community health to the amount of human activity in wetlands' adjacent watersheds; and relating the biomass of Cyanobacteria to the Total Phosphorus concentration in Canadian lakes.
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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 | 0.075 | 0.270 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".