Delayed Pantropical Carbon Sink Recovery Due To Asynchronous Post‐El Niño Photosynthesis and Respiration Trajectories
Bibliographic record
Abstract
Abstract Occurring in 2015/16, the strong El Niño has been shown to significantly influence the pantropical carbon fluxes with multiple lines of evidence. However, a comprehensive understanding on the regional and vegetation‐specific net ecosystem productivity (NEP) in response to this El Niño event and its recovery trajectories remain unclear. We revisited the controlling factors in the recovery of pantropical NEP from the 2015/16 El Niño until 2020 using a data assimilation framework. We estimated the pantropical net carbon emissions of 0.91 Pg C/yr in 2015 and the mean fraction of recovered area was 0.77 ± 0.07 during 2016–2020. Specifically, we found that temperature and radiation played dominant roles in controlling the recovery of forests' NEP via reducing photosynthesis. Furthermore, we revealed the compound climatic controls on enhanced ecosystem respiration offset NEP recovery over non‐forests. These results suggest that the divergence in carbon fluxes regulates the pantropical ecosystem net carbon uptake recovery.
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 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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".