Monte Carlo Arithmetic Instrumented DeepGOPlus Protein Function Predictions
Bibliographic record
Abstract
This dataset contains the perturbed protein function predictions by the DeepGOPlus model excluding the Diamond tool component. The model was perturbed with Verrou, an implementation of Monte Carlo Arithmetic (MCA), a stochastic arithmetic technique that injects noise into a program that simulates changes in a user's execution environment. The folders contain pkl files that can be read with the Pandas python library to load up dataframes containing the predictions and original values. Each file is one MCA sample run across the entire DeepGOPlus test set. The folder "Verrou_All" contains predictions where the entirety of the model was instrumented with MCA. The folder "Verrou_TF" contains predictions where only the Tensorflow library was instrumented with MCA. The folder "Fuzzy_Python" contains predictions where only the Python interpreter was instrumented with MCA. The folder "VPREC_Outbound_Mode" contains predictions where the virtual precision of the floating point operations was reduced witht the VPREC precision simulator tool in outbound mode. The folder "VPREC_Inbound_Mode" contains predictions where the virtual precision of the floating point operations was reduced witht the VPREC precision simulator tool in inbound mode. More information can be found by consulting this paper or this Github repository.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.040 |
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".