A precise method for the analysis of δ<sup>18</sup>O of dissolved inorganic phosphate in seawater
Bibliographic record
Abstract
A method for preparation and analysis of the oxygen isotope composition (δ18O) of dissolved inorganic phosphate (DIP) has been developed and preliminary results for water samples from various locations are reported. Phosphate is extracted from seawater samples by coprecipitation with magnesium hydroxide. Phosphate is further purified through a series of precipitations and resin separation and is ultimately converted to silver phosphate. Silver phosphate samples are pyrolitically decomposed to carbon monoxide and analyzed for δ18O. Silver phosphate samples weighing 0.7 mg (3.5 µmol oxygen) can be analyzed routinely with an average standard deviation of about 0.3‰. There is no isotope fractionation during extraction and blanks are negligible within analytical error. Reproducibility was determined for both laboratory standards and natural samples by multiple analyses. A comparison between filtered and unfiltered natural seawater samples was also conducted and no appreciable difference was observed for the samples tested. The δ18O values of DIP in seawater determined using this method range from 18.6‰ to 22.3‰, suggesting small but detectable natural variability in seawater. For the San Francisco Bay estuary DIP δ18O is more variable, ranging from 11.4‰ near the San Joaquin River to 20.1‰ near the Golden Gate Bridge, and was well correlated with salinity, phosphate concentration, and δ18O of water.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".