Evaluating GPP and Respiration Estimates Over Northern Midlatitude Ecosystems Using Solar‐Induced Fluorescence and Atmospheric CO<sub>2</sub> Measurements
Bibliographic record
Abstract
Abstract On regional to global scales, few constraints exist on gross primary productivity (GPP) and ecosystem respiration (Re) fluxes. Yet constraints on these fluxes are critical for evaluating and improving terrestrial biosphere models. In this study, we evaluate the seasonal cycle of GPP, Re, and net ecosystem exchange (NEE) produced by four terrestrial biosphere models and FLUXCOM, a data‐driven model, over northern midlatitude ecosystems. We evaluate the seasonal cycle of GPP and NEE using solar‐induced fluorescence retrieved from the Global Ozone Monitoring Experiment‐2 and column‐averaged dry‐air mole fractions of CO2 (XCO2) from the Total Carbon Column Observing Network, respectively. We then infer Re by combining constraints on GPP with constraints on NEE from two flux inversions. An ensemble of optimized Re seasonal cycles is generated using five GPP estimates and two NEE estimates. The optimized Re curves generally show high consistency with each other, with the largest differences due to the magnitude of GPP. We find optimized Re exhibits a systematically broader summer maximum than modeled Re, with values lower during June–July and higher during the fall than Re. Further analysis suggests that the differences could be due to seasonal variations in the carbon use efficiency (possibly due to an ecosystem‐scale Kok effect) and to seasonal variations in the leaf litter and fine root carbon pool. The results suggest that the inclusion of variable carbon use efficiency for autotrophic respiration and carbon pool dependence for heterotrophic respiration is important for accurately simulating Re.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".