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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".