Benchmarking terrestrial models. ENSEMBLES Deliverable 6.5
Bibliographic record
Abstract
This report marks both the culmination of work carried out under the banner of ENSEMBLES WP6.1 during 2006-2009, to develop and apply a universal set of benchmarks for terrestrial carbon cycle modelling, and the starting phase of an international collaborative activity called iLAMB (for international Land Atmosphere Model Benchmarking), which also brings in a parallel effort in the USA (Randerson et al. 2009). iLAMB has attracted the interest of the wider community of weather, climate and carbon cycle modellers in Europe, the US, Canada, Japan and Australia. It held its first planning meeting in Exeter in June 2009, and was showcased at the GEWEX-iLEAPS Open Science Conference in Melbourne in August of the same year. In addition to core support from ENSEMBLES to groups participating in WP6.1 (Met Office, LSCE, Bristol, Lund, Potsdam), the work described here has benefited from additional work financed by NERC’s Quantifying and Understanding the Earth System (QUEST) programme through its project Carbon Cycle Modelling, Analysis and Prediction (CCMAP), the Centre for Ecology and Hydrology (CEH), and the Met Office.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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; both teacher heads agree on what is shown here.
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".