Sensitivity tests of the integrated biosphere simulator to soil and vegetation characteristics in a pacific coastal coniferous forest
Bibliographic record
Abstract
Testing and sensitivity analysis of the Integrated Biosphere Simulator (IBIS) were performed for a range of vegetation and soil variables at a temperate coniferous forest site on eastern Vancouver Island, British Columbia, Canada. Vegetation structure and species composition, as well as seasonal changes in vegetation cover fraction and leaf area index, were imposed based on observed data. Simulated fluxes of sensible and latent heat, soil heat and net carbon exchange, and related estimates of soil temperature, soil moisture, and abiotic decomposition, were first compared to a complete year (1998) of half‐hourly observed data. The model reproduced observed daily, seasonal and yearly fluxes reasonably well, and was particularly successful in estimating the magnitude of net annual carbon uptake. Because of the high spatial variability in soil moisture content, however, it was difficult to obtain a complete assessment of model performance. Soil texture classification was found to have important effects on all fluxes, and particularly on estimates of soil decomposition, raising concerns about the effects of using spatially aggregated soils data for driving regional and global simulations. Simulation of net ecosystem exchange was also found to be highly sensitive to the value selected for canopy fractional cover and maximum carboxylase activity, Vmax, which suggests that environmental factors, such as limited nutrient availability and changes in vegetation type, will have important impacts on predictions of productivity and carbon budget.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".