FORest Canopy Atmosphere Transfer (FORCAsT) 2.0: model updates and evaluation with observations at a mixed forest site
Bibliographic record
Abstract
Abstract. The FORCAsT (FORest Canopy Atmosphere Transfer) model version 1.0 is updated to FORCAsT 2.0 by implementing five major changes, including (1) a change to the operator splitting, separating chemistry from emission and dry deposition, which reduces the run time of the gas-phase chemistry by 70 % and produces a more realistic in-canopy profile for isoprene; (2) a modification of the eddy diffusivity parameterization to produce greater and more realistic vertical mixing in the boundary layer, which ameliorates the unrealistic simulated end-of-day peaks in isoprene under well-mixed conditions and improves daytime air temperature; (3) updates to dry deposition velocities with available measurements; (4) implementation of the Reduced Caltech Isoprene Mechanism (RCIM) to reflect the current knowledge of isoprene oxidation; and (5) extension of the aerosol module to include isoprene-derived secondary organic aerosol (iSOA) formation. Along with the operator splitting, modified vertical mixing, and dry deposition, RCIM improves the estimation of first-generation isoprene oxidation products (methyl vinyl ketone and methacrolein) and some second-generation products (such as isoprene epoxydiols). Inclusion of isoprene in the aerosol module in FORCAsT 2.0 leads to a 7 % mass yield of iSOA. The most important iSOA precursors are IEPOX and tetrafunctionals, which together account for >86 % of total iSOA. The iSOA formed from organic nitrates is more important in the canopy, accounting for 11 % of the total iSOA. The tetrafunctionals compose up to 23 % of the total iSOA formation, highlighting the importance of the fate (i.e., dry deposition and gas-phase chemistry) of later-generation isoprene oxidation products in estimating iSOA formation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".