PredictiveEcology/SpaDES: v1.2.0.9011
Bibliographic record
Abstract
Known issues: https://github.com/PredictiveEcology/SpaDES/issues version 1.2.0.9011 Add filesOnly arg to shine(). This can be in preparation for publishing to www.shinyapps.io or other pages. Currently still alpha. Add POM: Pattern Oriented Modeling (#269). A simple interface to a simList object, allowing fitting parameters to data. add col arg to Plot (mimicks cols). Was lost from version 1.1.2. add .inputObjects functionality -- function that runs during simInit() to create default inputObjects add p as a namespaced shortcut for params: p(sim) would replace params(sim)$moduleName allow params(sim) & start(sim) & others in defineModule() by changing parse order in module metadata allow any arbitrary function to be used internally to Plot (e.g., barplot, plot, etc.) add arr argument to Plot(), allowing passing of arrangement allow title arg in Plot() to accept character for plot title change new arg in Plot(). Now it does one plot at a time, not whole device. Use clearPlot() to wipe whole device. Plot can use character passed to title as a title. add RandomFieldsUtils to Imports some additional functionality for factor rasters, incl. clickValues, legends correct for wide variety of types add explicit cl arg to parallel aware functions, for more control newModule gains new arguments type = c("child", "parent") and children = c(). See ?newModule (#300). checksums updated to use faster hashing algorithm (xxhash64) and now only requires a single hash value per file (#295) new Rstudio addin for 'newModule' (#298); requires Import of shiny (>= 0.13), miniUI (>= 0.1.1), and rstudioapi (>= 0.5)
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.363 | 0.440 |
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".