Lake Louise Water (USGS47): A new isotopic reference water for stable hydrogen and oxygen isotope measurements
Bibliographic record
Abstract
RATIONALE: Because of the paucity of isotopic reference waters for daily use, a new secondary isotopic reference material has been prepared from Lake Louise water from Alberta, Canada, for international distribution. METHODS: This water was filtered, homogenized, loaded into glass ampoules, sealed with a torch, autoclaved to eliminate biological activity, and measured by dual-inlet isotope-ratio mass spectrometry. This isotopic reference water is available by the case of 144 glass ampoules containing 5 mL of water in each ampoule. RESULTS: The δ(2)H and δ(18)O values of this reference water are -150.2 ± 0.5‰ and -19.80 ± 0.02‰, respectively, relative to VSMOW, on scales normalized such that the δ(2)H and δ(18)O values of SLAP reference water are, respectively, -428 and -55.5‰. Each uncertainty is an estimated expanded uncertainty (U = 2uc) about the reference value that provides an interval that has about a 95% probability of encompassing the true value. CONCLUSIONS: This isotopic reference material, designated as USGS47, is intended as one of two isotopic reference waters for daily normalization of stable hydrogen and stable oxygen isotopic analysis of water with a mass spectrometer or a laser absorption spectrometer. Published in 2013. This article is a U.S. Government work and is in the public domain in the USA.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".