Advancing towards high-quality water isotope measurements in ice cores: a micro-destructive approach using Laser Ablation coupled with Cavity Ring Down Spectroscopy
Bibliographic record
Abstract
Addressing the intricate challenges of water isotope analysis in polar ice cores, especially in extracting detailed climate records from older and thinner ice layers, the innovative integration of Laser Ablation (LA) with Cavity Ring Down Spectroscopy (CRDS) is introduced. The micro-destructive LA technique, which employs a nanosecond excimer pulsed laser operating at 193 nm for ice surface irradiation, demonstrates potential in achieving continuous, high-resolution sampling and gas phase sample generation, complementing the CRDS analyzer's precision in measuring water isotopes in gaseous state. Recent advancements include the successful adaptation of an existing LA system, previously coupled with an Inductively Coupled Plasma - Mass Spectrometer (ICP-MS) for ice core impurity analysis, to establish a connection with the CRDS analyzer. This was accomplished by making adjustments to the coupling procedure and laser parameters, to ensure efficient gas sample generation and robust delivery for water isotope analysis. A method for creating ice standard samples by transforming liquid water standards into ice yielded ice isotope standards, crucial for setting up initial measurement protocols. Their implementation on both standard ice samples and sections of ice cores revealed valuable insights into areas for improvement. This represents a significant step towards establishing a reliable method for high-quality water isotope analysis in ice cores, aiming to significantly enrich our understanding of long-term climate trends.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".