Requirements and system design for the new CIS MLR forecasting system (SIPS)
Bibliographic record
Abstract
SIPS will replace the current regression-based forecasting system, MLR-Model, that is run as a research tool and experimental operational tool at the Canadian Ice Service. The existing system is a large and complex collection of Fortran programs and scripts written in numerous languages (python, bsh, ksh, sed, etc.) that rely on a very rigid configuration both from an operating system as well as from a data organization point of view. The purpose of developing a new software system is to ease portability between computers, simplify the pre-processing and post-processing of input/output data, simplify the data structure, expand the application of the model, improve visualization, eliminate dependence on the proprietary NAG library and make the system accessible to other ice centers. All functionality found in the existing system will be retained. In addition to the above stated improvements the SIPS system will include the following new functionality. the ability to forecast on different time-scales a mechanism to easily integrate new predictor datasets visualization and analysis tools
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.023 |
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".