Response of Carbon Sink Dynamic Behaviors to River Flood in Karst Area – A Case Study in the Li River of Guilin
Bibliographic record
Abstract
Li River appeared successively three times floods during May 8-17, 2012. We were high-frequency monitoring during the flood once every hour, real time monitoring the pH value, water temperature, EC(electrical conductivity), pCO2(carbon dioxide partial pressure), HCO3- and flow rate, analyzing karst carbon sinks dynamic changes during the flood. It was found that river hydrochemistry and karst carbon sinks in different stages with different variations in Li River. These floods were divided into 5 stages to discuss, researches have shown: AtIand Vstage the river hydrochemistry is not subjected to flooding, pCO2 and pH value, water temperature has distinct characteristics of diurnal variation. EC, flow rate and HCO3- is relatively stable; II, III and IV stage appear different changes characteristics are due to effects of flood, flow rate and HCO3- have a positive correlation at IV stage, with opposite of stage II and III stage. We use water chemistry-runoff method to calculate the amount of carbon sinks in the flood, found in the flood related coefficients between carbon sink and HCO3-, flow rate respectively 0.87 and 0.33. The carbon sink is 3491.06 t C during the flood monitoring, in which carbon sink at IVstage in flood are 4.52 times prior to the flood, which account for that carbon sink in the flood is much larger than the front of flood.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".