Validation of the Integrated Biosphere Simulator over Canadian deciduous and coniferous boreal forest stands
Bibliographic record
Abstract
Data collected during the Boreal Ecosystem‐Atmosphere Study (BOREAS) at four different forest stands were used to test surface energy and carbon fluxes simulated by the Integrated Biosphere Simulator (IBIS). These stands included deciduous and conifer species and were located in both the BOREAS northern and southern study areas. Two runs were made: one using the original IBIS model and the other using a version modified to consider an organic soil layer (OSL) covering the mineral soil surface. Results show that the inclusion of the OSL substantially improved the simulation of soil heat flux, as well as of temperature and moisture in the topmost soil layer. Simulations show that latent and sensible heat fluxes, and net ecosystem exchange of carbon, were not affected appreciably by the presence of a thin (10 cm or less) OSL covering the forest floor. With a thick (50 cm) OSL, however, simulation of latent heat flux and net ecosystem exchange of carbon was substantially improved. Consideration of the OSL in the model also led to better simulation of the onsets of soil thawing. Correct estimation of heat diffusion to deep soil through thick organic layers requires a parameterization that accounts for the state of the organic material decomposition. Simulations presented here also show the necessity for using detailed information on soil physical properties for better evaluation of model performance.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".