INVESTIGATING MAXIMUM TEMPERATURES USING RAMAN SPECTROSCOPY OF CARBONACEOUS MATERIAL IN CONODONTS: A COMPARISON WITH THE CONODONT COLOR ALTERATION INDEX
Bibliographic record
Abstract
The conodont Color Alteration Index (CAI) has been widely used to provide estimates of maximum temperature in studies of thermal maturity. Despite this, several drawbacks of CAI have been identified. Firstly, there is significant uncertainty in the identification of CAI; methods have been proposed to make the quantification of CAI more rigorous, but they are not routinely applied. Secondly, CAI is not just related to temperature but also to duration of heating; as this duration is commonly unknown, there is significant uncertainty in estimates of maximum temperature based on CAI. Finally, CAI has been shown to be affected not just by heat, but also by diagenesis; although no mechanism by which diagenesis would affect CAI has been clearly defined. Raman spectroscopy of carbonaceous material (RSCM) has been extensively used to calculate maximum temperatures experienced by organic matter in sedimentary rocks. As carbonaceous matter is heated, it becomes progressively more graphitized, leading to an increase in structural order that can be measured by Raman spectroscopy. A set of 49 conodont specimens with varying CAI from several localities in British Columbia, Canada, were analysed with Raman spectroscopy, and maximum temperatures for these specimens were calculated independently from CAI by using the Iterative Fitting of Raman Spectra (IFORS) technique. Maximum temperatures calculated with IFORS often show significant deviation from the temperatures estimated by CAI alone; CAI thus appears to be an inconsistent indicator of maximum temperature. The greatest variation between IFORS and CAI temperatures occurs in specimens with CAI of 2.5, 4.5, and 6.0. Analysis of minor and trace element compositions of these specimens shows that they also have relatively high concentrations of exogenous cations. Additional investigation of the biomineral component of conodonts using Raman spectroscopy shows that diagenetic alteration is pervasive. We propose that conodont color is affected by the diagenetic uptake of exogenous cations into conodonts, and, as a result, CAI does not solely reflect changes in temperature. In contrast, RSCM with IFORS is a relatively inexpensive and minimally-invasive technique for the independent calculation of maximum temperatures, which can replace or supplement CAI.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".