starve: An R package for spatio‐temporal analysis of research survey data using nearest‐neighbour Gaussian processes
Bibliographic record
Abstract
Abstract Spatio‐temporal datasets that are difficult to analyse are commonly derived from ecological surveys. There are software packages available to analyse these datasets, but many of them require advanced coding skills. There is a growing need for easy‐to‐use packages that researchers can use to analyse common ecological datasets. We develop a particular generalized linear mixed model framework for spatio‐temporal point‐referenced data that is flexible enough to accommodate data from most ecological surveys while being structured enough to facilitate analyses without advanced coding. Our implementation in the starve package uses a computationally efficient version of a nearest‐neighbour Gaussian process enabling analysis of relatively large datasets. A tutorial analysis of a Carolina wren survey presents a recommended workflow for analyses while showcasing the capabilities of the package. Our model and package are tools that can easily be added to researchers' routine to help make sense of data from ecological surveys. We emphasize the ability of our model to create fine‐scale spatio‐temporal predictions which can then be used to visualize and identify important trends in species distributions.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".