In situ U-Pb and Nd-Hf-(Sr) isotopic investigations of zirconolite and calzirtite
Bibliographic record
Abstract
Although the calcium- and titanium-bearing zirconsilicates, zirconolite and calzirtite, are common minerals in carbonatites little is known regarding their trace element or isotopic characteristics. Available data indicate that they can have significant contents of U. Pb, Nb, Ta, Zr, Hf, rare earth elements (REEs), minor Sr and low Rb/Sr and Lu/Hf ratios. Their compositions indicate that they have potential for U Pb age determination together with Nd Hf and possibly Sr isotopic analyses. In this study, zirconolite and calzirtite from carbonatites occurring at Phalaborwa (South Africa), Prairie Lake (Canada), Afrikanda and Kovdor (Kola, Russia), Gull (Siberia, Russia) and Jacupiranga (Brazil) were selected for an isotopic analysis by in situ techniques. Ion microprobe analyses using a CAMECA 1280 ion microprobe show that reliable Pb Pb ages can be obtained from these minerals as they have extremely high U concentrations, coupled with negligible common Pb. Although these minerals have low Rb/Sr ratios, their low Sr concentrations, in most cases, render in situ Sr isotopic analyses difficult. In contrast, their high Sm, Nd and Hf concentrations, with generally low Lu/Hf ratios, permit precise in situ isotopic analysis of Nd and Hf.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".