Physicochemical parameters of groundwater in coastal sandy aquifer
Bibliographic record
Abstract
In the Yangjiang profile, a total of 29 (27 groundwater samples and 2 surface water samples: seawater and aquaculture wastewater) and 32 (29 groundwater samples and 2 surface water samples: seawater and aquaculture wastewater) samples were collected on August 12, 2020 (wet season) and November 8, 2020 (dry season), respectively. The measurement procedures for the trace metals (As, Ba, Cd, Cr, Fe, Mn, Pb, and Zn) in groundwater samples were described elsewhere (Luo et al., 2021). In brief, the samples with high salinity (>1) were first diluted and then analyzed using inductively coupled plasma mass spectrometry (ICP–MS) (Agilent 7900 Series, USA). The detection limits (μg/L) of trace metals were 0.12, 0.20, 0.05, 0.11, 0.82, 0.12, 0.09, and 0.67 for As, Ba, Cd, Cr, Fe, Mn, Pb, and Zn, respectively. A mixed standard solution and two standard reference materials were used to construct the calibration curve and control the analytical quality, respectively. The recoveries of the standard reference materials were 105.4%, 101.7%, 102.6%, 104.0%, and 103.4% for As, Cd, Cr, Pb, and Zn, respectively, indicating that the measured results were within the range of the certified values (Luo et al., 2021). Reagent blanks were determined for each sample analysis and trace metals were undetectable therein.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".