Management Strategy Evaluation: Hindcast Model Output, 1979-2012. Version 1.0
Bibliographic record
Abstract
This dataset contains Annual estimated results from Climate-Enhanced Age-based model with Temperature-specific Trophic Linkages and Energetics (CEATTLE) model and individual age-structured models for hindcast period 1979-2012. Four climate scenarios are considered for MSMtA and MSMtB because temperature influences weight-at-age and the predation mortality function in MSMt. Forecasts were based on downscaled temperature data forced ROMS model (Herman et al. 2013). The ROMS-NPZD model (Hermann et al. 2013) was set-up to run in forecast mode using input from selected Intergovernmental Panel on Climate Change climate models that performed well for the Eastern Bering Sea. The four scenarios are: Future temperature is constant and equal to the mean of historical temperatures (temperature scenario 1), and future temperature in each projection year is the mean summer water column temperature predicted from a ROMS model for the Bering Sea forced by three statistically downscaled global climate models based on the IPCC A1B climate scenario (Wang et al., 2010), including: (temperature scenario 2) ECHO-G version 4, T30 resolution model (Legutke and Voss, 1999), (temperature scenario 3) CCMA model (Canadian Centre for Climate Modelling and Analysis CGCM3-t47; Flato et al., 2000, Flato and Boer 2001, Kim et al. 2002, 2003), (temperature scenario 4) MIROC 3.2 (Watanabe et al., 2011, K-1 model developers, 2004). Average temperature was extracted from these simulations to use as input forcing files in CEATTLE. A single realization of each of these three climate scenarios was used for atmospheric forcing and oceanic boundary conditions for the regional ROMS forecasts of the Bering Sea (present to 2040).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.015 |
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".