Bibliographic record
Abstract
Source codes (and data) for continuous isotope mixing model, CrisPy. MixSIAR scripts, for model comparison, are also included. For CrisPy, two test files are included, one is virtual test (CrisPy_virtual_tests) another is field test (CrisPy_Field_tests). CrisPy is written by PyMC (version 5.6.1) under Python 3.11. Installation and introduction of PyMC can be found at https://www.pymc.io/welcome.html. Other required packages including Numpy, pandas, matplotlib, pytensor, arviz, scipy, and xarray. Anaconda is strongly recommended. For different prior settings: adjust Line-17 in the source code. Important outputs: Uptake profile :y (depth vector is xy) 2.5% CI of y: mean_PDF_25 97.5% CI of y: mean_PDF_975 Uptake proportion : ycdf (depth vector is x4) SD of uptake proportion: ycdf_std Shape parameters information: summary2 Regarding MixSIAR, it is a R library and widely used for solving mixing problems. Installation and introduction can be found at https://cran.r-project.org/web/packages/MixSIAR/index.html. Two scripts are included, virtual_test and Field_test. Notes related to MixSIAR script: Set up working path in Line-17. Then, load the libraries and do the following steps to force MixSIAR output variance (or covariance) information for posterior predictive checking: Load MixSIAR (library("MixSIAR")) Run " trace("run_model", edit=TRUE) " in console In line-74 , change "jags.params <- c("p.global", "loglik")" to "jags.params <- c("p.global", "loglik", "Sigma")" for residual error structure, and save. (For residual*process, variable name is "Sigma.ind") Rerun the script. For different prior scenarios, adjust Line-19 Codes below line-95 are prior and posterior predictive checking. Outputs are named as plant_post and plant_post. Excel files can be found under the path. For any questions related to CrisPy and MixSIAR, please send an email to Han (haf033@usask.ca). Thank you for your interest!
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.392 | 0.319 |
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".