Bibliographic record
Abstract
In this report we present simulations of two dynamic vegetation models (JULES and TFS) at different study sites across the Mediterranean Basin. The field data and the way that these data have been used to parameterize the two models are described in the accompanying MEDIT Deliverable 4. The aim here is to explore the ability of the models to accurately predict vegetation properties at areas surrounding the Mediterranean basin and in particular the forest types that are the focus of the MEDIT project i.e. Mediterranean Lowland Coniferous Forests (MLC) with typical species including: Pinus halepensis and Pinus brutia, Mediterranean Evergreen Broadleaved Forests (MEB) with typical species including: Quercus coccifera, Quercus ilex, Pistacia lentiscus, Phillyrea latifolia, Arbutus unedo and Arbutus andrachnae, Mediterranean Mountainous Coniferous Forests (MMC) with typical species including: Abies cephalonica, Abies borisii-regis, Pinus nigra and Pinus sylvestris, and Mediterranean Deciduous Broadleaved Forest (MDB) with typical species including: Quercus frainetto, Quercus cerris, Castanea sativa, Ostrya carpinifolia, Carpinus orientalis, Fraxinus ornus and Fagus sylvatica.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".