Characteristics of CO<sub>2</sub>, water vapor, and energy exchanges at a headwater wetland ecosystem of the Qinghai Lake
Bibliographic record
Abstract
Ecosystem carbon dioxide (CO2), water vapor, and heat exchanges in alpine wetlands on the Qinghai–Tibetan Plateau are not comprehensively understood. Thus, we studied variability of net ecosystem CO2 exchange (NEE), ecosystem respiration (Re), gross primary production (GPP), evapotranspiration (ET), and heat fluxes over a headwater wetland ecosystem in the Qinghai Lake region. Results showed that the headwater wetland ecosystem was net CO2 absorption on the annual scale, in which monthly NEE, GPP, and Re in two consecutive years varied from −165.16 to 93 g CO2 m−2 mo−1, 6.66 to 384.45 g CO2 m−2 mo−1, and 6.9 to 232.02 g CO2 m−2 mo−1, respectively. The monthly ET from June to September was smaller than precipitation; these results reversed in the remaining months. Annual ET was 362.1 mm in 2015 and 324.96 mm in 2016. The net radiation (Rn), sensible heat (H), latent heat (LE), and ground heat (G) fluxes showed similar monthly patterns. Values of monthly average half-hour Rn, H, LE, and G at the daytime showed Rn > LE > H > G, and the time of the monthly half-hour G peak obviously lagged the Rn, H, and LE. Monthly average Bowen ratios were <1 from May to October, but it reversed in the rest of the months.
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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 | 0.000 | 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 | 0.000 |
| 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 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".