An Image Mapping Approach to U‐Pb LA‐ICP‐MS Carbonate Dating and Applications to Direct Dating of Carbonate Sedimentation
Bibliographic record
Abstract
Abstract We present a new approach to laser ablation inductively coupled plasma mass spectrometry (LA‐ICP‐MS) U‐Pb dating of carbonates based on selection and pooling of pixels from 2‐D elemental and isotopic ratio maps. This image mapping technique is particularly useful for targeting subdomains in samples with complex geological histories. Key major and trace elements that are sensitive to detrital components, postformational fluid ingress, mineralogical changes, or diagenetic overprinting are measured along with the Pb and U isotopic data. Laser sampling is undertaken along successive linear rasters that are compiled into maps using the Monocle add‐on for Iolite , with one pixel in the map corresponding to one time slice of the time‐resolved signal. These element, element ratio, and isotope ratio maps can be overlain over photomicrographs or scanning electron microscopy images to spatially link compositional data to textural and structural features. The pixels corresponding to likely homogeneous age domains can be isolated by applying appropriate selection criteria (e.g., Th < 0.3 ppm, Mg/Ca < 0.004) and pooled into pseudo‐analyses using a proxy for the parent/daughter ratio (e.g., 207 Pb/ 235 U, 238 U/ 208 Pb) to retrieve the largest possible spread of the data points on isochron diagrams. The approach is best suited for analytical setups capable of rapidly or simultaneously scanning over a large mass range and can yield a precision of ±1% or better on quadrupole instruments depending on U concentration, 238 U/ 204 Pb, and age of the sample. The sample‐specific filtering criteria for selection and rejection of data and their rationale can be reported, resulting in more transparency with regard to data processing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".