Multi-metric evaluation of an ensemble of biogeochemical models for the estimation of organic carbon content in long-term bare fallow soils
Bibliographic record
Abstract
As part of benchmarking actions at international level (FACCE-JPI project CN-MIP), the C-MIP action was initiated in 2016 to address the question of whether ensemble modelling could bring some improvement to the simulation of soil organic carbon (SOC) dynamics. A multi-model ensemble with 25 process‐based integrated C-N models was implemented to compare simulations (before and after model calibration) to SOC data from a network of six long-term bare fallow experimental sites (one site with two options) in Europe. To evaluate single models and the model ensemble, multiple evaluation metrics were aggregated into a single modular indicator, not only accounting for the agreement between model estimates and actual data but also taking into account their structural complexity. Illustrative results of simulated against observed SOC dynamics while also discussing the potential of the multi-metric aggregated indicator to help identifying areas where structural changes in models may be needed to better represent such dynamics.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".