segmented: Regression Models with Break-Points / Change-Points Estimation (with Possibly Random Effects)
Why this work is in the frame
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Bibliographic record
Abstract
Fitting regression models where, in addition to possible linear terms, one or more covariates have segmented (i.e., broken-line or piece-wise linear) or stepmented (i.e. piece-wise constant) effects. Multiple breakpoints for the same variable are allowed. The estimation method is discussed in Muggeo (2003, <<a href="https://doi.org/10.1002%2Fsim.1545" target="_top">doi:10.1002/sim.1545</a>>) and illustrated in Muggeo (2008, <<a href="https://www.r-project.org/doc/Rnews/Rnews_2008-1.pdf" target="_top">https://www.r-project.org/doc/Rnews/Rnews_2008-1.pdf</a>>). An approach for hypothesis testing is presented in Muggeo (2016, <<a href="https://doi.org/10.1080%2F00949655.2016.1149855" target="_top">doi:10.1080/00949655.2016.1149855</a>>), and interval estimation for the breakpoint is discussed in Muggeo (2017, <<a href="https://doi.org/10.1111%2Fanzs.12200" target="_top">doi:10.1111/anzs.12200</a>>). Segmented mixed models, i.e. random effects in the change point, are discussed in Muggeo (2014, <<a href="https://doi.org/10.1177%2F1471082X13504721" target="_top">doi:10.1177/1471082X13504721</a>>). Estimation of piecewise-constant relationships and changepoints (mean-shift models) is discussed in Fasola et al. (2018, <<a href="https://doi.org/10.1007%2Fs00180-017-0740-4" target="_top">doi:10.1007/s00180-017-0740-4</a>>).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 it