An assessment of geographical distribution of different plant functional types over North America simulated using the CLASS–CTEM modelling framework
Bibliographic record
Abstract
Abstract. The performance of the competition module of the CLASS–CTEM (Canadian Land Surface Scheme and Canadian Terrestrial Ecosystem Model) modelling framework is assessed at 1° spatial resolution over North America by comparing the simulated geographical distribution of its plant functional types (PFTs) with two observation-based estimates. The model successfully reproduces the broad geographical distribution of trees, grasses and bare ground although limitations remain. In particular, compared to the two observation-based estimates, the simulated fractional vegetation coverage is lower in the arid southwest North American region and higher in the Arctic region. The lower-than-observed simulated vegetation coverage in the southwest region is attributed to lack of representation of shrubs in the model and plausible errors in the observation-based data sets. The observation-based data indicate vegetation fractional coverage of more than 60 % in this arid region, despite only 200–300 mm of precipitation that the region receives annually, and observation-based leaf area index (LAI) values in the region are lower than one. The higher-than-observed vegetation fractional coverage in the Arctic is likely due to the lack of representation of moss and lichen PFTs and also likely because of inadequate representation of permafrost in the model as a result of which the C3 grass PFT performs overly well in the region. The model generally reproduces the broad spatial distribution and the total area covered by the two primary tree PFTs (needleleaf evergreen trees, NDL-EVG; and broadleaf cold deciduous trees, BDL-DCD-CLD) reasonably well. The simulated fractional coverage of tree PFTs increases after the 1960s in response to the CO2 fertilization effect and climate warming. Differences between observed and simulated PFT coverages highlight model limitations and suggest that the inclusion of shrubs, and moss and lichen PFTs, and an adequate representation of permafrost will help improve model performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".