Bibliographic record
Abstract
Should the need to model the relationship between bivariate data and a response variable arise, two-dimensional (2D) Gaussian models are often the most appropriate choice.For example, Priebe et al. (2003) characterized motion-sensitive neurons in the brains of macaques by fitting 2D-Gaussian functions to neurons' response rates as spatial and temporal frequencies of visual stimuli were varied.The width and orientation of these fitted 2D-Gaussian surfaces provides insight on whether a neuron is "tuned" to particular spatial or temporal domains.Twodimensional Gaussians are also used in other scientific disciplines such as physics (Kravtsov & Berczynski, 2004;Z. Wu & Guo, 1998), materials sciences (Riekel et al., 1999), and image processing (Hanumantharaju et al., 2013;Ketenci & Gencturk, 2013), particularly in medical imaging (Qadir et al., 2021;J. Wu et al., 2019).Fitting 2D-Gaussian models to data is not always a straightforward process, as finding appropriate values for the model's parameters relies on complex procedures such as non-linear least-squares.gaussplotR is an R package that is designed to fit 2D-Gaussian surfaces to data.Should a user supply bivariate data (i.e., x-values and y-values) along with a univariate response variable, functions within gaussplotR will allow for the automatic fitting of a 2D-Gaussian model to the data.Fitting the model then enables the user to characterize various properties of the Gaussian surface (e.g., computing the total volume under the surface).Further, new data can be predicted from models fit via gaussplotR, which in combination with the package's plotting functions, can enable smoother-looking plots from relatively sparse input data.In principle, tools within gaussplotR have broad applicability to a variety of scientific disciplines.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.145 | 0.092 |
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".