Bibliographic record
Abstract
CICE6.2.0 is a major update of CICE6.1.4 from December, 2020. It includes Icepack1.2.5. This release includes updates to the default model initial conditions, updates to the default JRA55 forcing, and an update gx1 ocean mixed layer forcing file. It also includes an update to the time manager and a tool to generate JRA55 forcing data offline. A probabilistic grounding scheme was also added to the landfast ice implementation. Several minor bug fixes were also included and the default emissivity value was changed to 0.985. This implementation should generate the same climate as prior versions in coupled systems. However, the emissivity change should be noted in particular. In addition, there were changes to the time manager that could impact how models are coupled. In particular, the prognostic time manager variables are now myear, mmonth, mday, and msec and the timestep counter is diagnostic. These changes are documented in the user guide, https://cice-consortium-cice.readthedocs.io/en/cice6.2.0/user_guide/ug_implementation.html#time-manager-and-initialization. The standalone model results are different from prior versions due to the new initial condition and forcing even though the climate is similar. Major changes: Update the Time Manager #566, #586 Add probabilistic grounding scheme for landfast ice #565 and update landice tests #586 Update the gx1 initial condition file based on the JRA55 spinup to Jan 1, 2005 and update the gx1prod namelist setup #586 Bug fixes: Fix hmix minimum value #586 Fix halo fill for padded blocks, fix some uninitialized data #579 Fix reading of mixed layer ocean model dataset #578 and update gx1 SOM dataset #586 Use cell-centered ice velocity for high frequency wind stress calculation #576 Proper configuration of zsalinity parameterization #549 Remove unused private variable for OpenMP #561 Correct input slices and time interpolation of JRA forcing for multi-year runs #562 Enhancements: Update 1D EVP kernel #561 Improve maxblocks internal computation and add some additional debugging checks as well as a namelist flag #587 Add JRA55 dataset generation tool and add documentation #582 Update Icepack to newer version #580, #590 Change landfast ice parameter value to published value #574 Adjust emissivity to 0.985, set default NBGCLYR=1 #553 Add test for zsalinity parameterization #548 Add automated Github Actions testing on Pull Requests and Pushes to master and releases #555, Update github Actions automated testing script #586 Update color labels in test reporting script #586 Update conda macos port #590 Port to Compy #581 Documentation Add documentation for the JRA55 dataset generation tool #582 Update landfast ice documentation #574 Update readthedocs requirements for bibtex #545 Update copyright year #570 #571
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.103 | 0.126 |
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".