Statistical methods for estimating ecological breakpoints and prediction\n intervals
Bibliographic record
Abstract
The relationships among ecological variables are usually obtained by fitting\nstatistical models that go through the conditional means of the dependent\nvariables. For example, the nonparametric loess and the parametric piecewise\nlinear regression models, which pass through the conditional mean of the\nresponse variable given the predictor, are used to analyze simple to complex\nrelationships among variables. We used loess and bootstrapped confidence\ninterval to subjectively identify the number and positions of potential\necological breakpoints in a bivariate relationship, and a piecewise linear\nregression model (PLRM) to quantitatively estimate the location of breakpoints\nand the associated precision. We also estimated breakpoint location and\nprecision using a piecewise linear quantile regression model (PQRM), which is\nfitted to the quantiles of the conditional distribution of the response\nvariable given the predictor and provides much richer information in terms of\nestimating relationships and breakpoints. We compared the precision of\nbreakpoints estimated by PQRM relative to PLRM. We compared the precision of\nthe methods using two examples from the ecological literature suspected to\nexhibit multiple breakpoints: relating a Fish Index of Biotic Integrity (an\nindex of wetlands' fish community 'health') to the amount of human activity in\nwetlands' adjacent watersheds; and relating the biomass of cyanobacteria to the\ntotal phosphorus concentration in Canadian lakes. Statistically significant\nbreakpoints were detected for both datasets, demarcating the boundaries of\nthree line segments with markedly different slopes. We recommend the piecewise\nlinear quantile regression as an effective means of characterizing bivariate\nenvironmental relationships where the scatter of points represents natural\nenvironmental variation rather than measurement error.\n
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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 | 0.077 | 0.286 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".