Representing Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: Part 2. Carbon Cycling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Grasslands are one of the most widely distributed and abundant vegetation types globally, and land surface models struggle to accurately simulate grassland carbon dioxide, energy, and water fluxes. Here we hypothesize that this is due to land surface models having difficulties in reproducing grassland phenology, in particular in response to the seasonal and interannual variability of precipitation. Using leaf area index (LAI), net primary productivity, and flux data at 55 sites spanning climate zones, the aim of this study is to evaluate a novel prognostic phenology model (Simple Biosphere Model, SiB4) while simultaneously illustrating grassland relationships across precipitation gradients. Evaluating from 2000 to 2014, SiB4 predicts daily LAI, carbon, and energy fluxes with root‐mean‐square errors < 15% and individual biases <10%; however, not including management likely reduces its performance. Grassland mean annual LAI increases linearly with mean annual precipitation, with both SiB4 and the Moderate Resolution Imaging Spectroradiometer (MODIS) showing a 0.13 increase in LAI per 100‐mm increase in precipitation. Both gross primary production and ecosystem respiration increase with growing season length by ∼8.5 g C m −2 per day, with SiB4 and Fluxnet estimates within 18%. Despite differences in mean annual precipitation and growing season length, all grassland sites shift to seasonal carbon sinks one month prior to peak uptake. During a U.S. drought, MODIS and SiB4 had nearly identical LAI responses, and the LAI change due to drought was less than the LAI change across the precipitation gradient, indicating that grassland drought response is not as strong as the overlying climate response.
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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 | 0.001 | 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 | 0.000 |
| 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 it