Uncertainty Evaluations on the Measurement of “Carbon” in the Research on Karst Carbon Sink Effect - A Case for Dissolved Inorganic Carbon
Bibliographic record
Abstract
In order to set up an evaluation method for the uncertainty in determining dissolved inorganic carbon (DIC) in karst river basin by Non-dispersive Infrared (NDIR) absorption detection technique, the continuous propagation model of uncertainty is used to evaluating the uncertainties from DIC measurement in two typical karst groundwater samples. The main steps are as follows: to fit the calibration curve by means of double-error regression firstly; and then to quantify each uncertainty component in the evaluation process; lastly to obtain the synthetic uncertainty model for DIC determination results. Calculations through experimental results show that: (1) the main sources of measurement uncertainty derive from the sub-uncertainties of calibration solutions, calibration curve fitting and measurements process; (2) the lower the DIC content in groundwater samples, the greater the relative uncertainty of measurement results, and the sub-uncertainty from the fitting of calibration curve gives one major contribution to the total uncertainty.
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 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 | 0.003 | 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 teacher head, 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".