Bibliographic record
Abstract
Generalized additive models (GAMs, Hastie & Tibshirani, 1990;Wood, 2017) are an extension of generalized linear models that allows the effects of covariates to be modelled as smooth functions.GAMs are increasingly used in many areas of science (e.g.Pedersen, Miller, Simpson, & Ross, 2019;Simpson, 2018) because the smooth functions allow nonlinear relationships between covariates and the response to be learned from the data through the use of penalized splines.Within the R (R Core Team, 2024) ecosystem, Simon Wood's mgcv package (Wood, 2017) is widely used to fit GAMs and is a Recommended package that ships with R as part of the default install.A growing number of other R packages build upon mgcv, for example as an engine to fit specialised models not handled by mgcv itself (e.g.GJMR, Marra & Radice, 2023), or to make use of the wide range of splines available in mgcv (e.g.brms, Bürkner, 2017).The gratia package builds upon mgcv by providing functions that make working with GAMs easier.gratia takes a tidy approach (Wickham, 2014) providing ggplot2 (Wickham, 2016) replacements for mgcv 's base graphics-based plots, functions for model diagnostics and exploration of fitted models, and a family of functions for drawing samples from the posterior distribution of a fitted GAM.Additional functionality is provided to facilitate the teaching and understanding of GAMs.The overall aim of gratia is to abstract away some of the complexity of working with GAMs fitted using mgcv to allow researchers to focus on using and interrogating their model rather than the technical R programming needed to achieve this. Generalized additive modelsA GAM has the formwhere observations 𝑦 𝑖 are assumed to be conditionally distributed 𝒟 with expectation 𝔼(𝑦 𝑖 ) = 𝜇 𝑖 and dispersion parameter 𝜙.The expectation of 𝑦 𝑖 is given by a linear predictor of strictly parametric terms, whose model matrix is A 𝑖 with parameters 𝛾, plus a sum of 𝑗 = 1, … , 𝐽 smooth functions of covariates 𝑓 𝑗 ().𝑔() is a link function mapping values on the linear predictor to the scale of the response.The smooth functions 𝑓 𝑗 are represented in the GAM using penalised splines, which are themselves formed as weighted sums of basis functions, 𝑏 𝑘 (), (De Boor, 2001)
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.094 | 0.039 |
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".