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Record W4220799013 · doi:10.5194/egusphere-egu22-61

Central ages, mono-kinetic, or multi-kinetic? Assessing detrital AFT thermochronology in a Cretaceous foreland basin

2022· preprint· en· W4220799013 on OpenAlexaffabout
Jennifer Spalding, David Schneider, J. W. Powell

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsGeological Survey of CanadaNatural Resources CanadaUniversity of Ottawa
Fundersnot available
KeywordsThermochronologyForeland basinFission track datingGeologyPopulationGeothermal gradientCretaceousClosure temperaturePaleontologyStructural basinTectonicsZircon

Abstract

fetched live from OpenAlex

Apatite fission track (AFT) thermochronology can resolve thermal histories over a temperature window that spans 50–150°C. Herein, we present a case study from the Peel Plateau, a foreland basin of the Mackenzie Mountains, northern Canadian Cordillera, where we compare three interpretations from a single AFT sample: 1) the central age, and thermal history modelling of 2) a mono-kinetic population and 3) a multi-kinetic population. The AFT sample is derived from a lithic wacke with an Albian depositional age and yields an AFT central age of 67 ± 9 Ma (n: 39). Despite central ages often being interpreted to reflect a sample’s 'cooling age,' laboratory experiments have demonstrated that fission tracks anneal over a wide temperature range, corresponding to the partial annealing zone (PAZ). AFT ages from samples that have undergone multiple burial and exhumation events likely represent partial annealing due to their protracted residence in the PAZ and require thermal history modelling to derive a meaningful geologic interpretation. It is unlikely the Peel Plateau experienced a simple thermal history with rapid cooling through the PAZ, as a regional unconformity separates the Albian and Cenomanian strata across much of the region. Thus, the central age does not necessarily reflect a meaningful thermal event. Thermal modelling the data as a mono-kinetic population predict peak burial temperatures of 118–166°C at 65–92 Ma, however, the sample fails the χ2, indicating it does not comprise a single statistical age population. Intra-sample age dispersion is often indicative of samples that comprise multi-kinetic population and grain specific chemistry is known to strongly influence apatite’s annealing kinetics. Most notably apatite with high F content will undergo thermal annealing at lower temperatures than apatite with high Cl content, although numerous other elements (e.g. OH, Mn, Fe) are known to effect annealing kinetics. The rmro parameter was developed through laboratory annealing experiments, and accounts for compositional controls on apatite’s kinetic behaviour. Apatite grain chemistry was measured via EMPA methods to calculate rmro values and used to separate AFT samples into two kinetic populations (resulting in pooled ages of 36 ± 5 Ma and 103 ± 12 Ma) that both pass the χ2 test. Pooled ages incorporate information of the grains’ pre-depositional history and U-Pb dating can serve as an additional tool to decipher between different kinetic populations. These populations then act as independent thermochronometers capable of resolving different temperature windows. Compared to thermal history model results from mono-kinetic data, the multi-kinetic model predicts significantly lower burial temperatures of 83–93°C, which may have occurred over a longer duration (33–88 Ma). The central age for this sample overlaps with the timing of peak burial predicted by the multi-kinetic model, therefore does not inform us about the cooling history. Ultimately, the purpose of this study is to highlight the importance of assessing multi-kinetic behaviour in sedimentary samples, as these interpretations provide statistically significant age populations and robust thermal history models. Thermal history models that ignore multi-kinetic behaviour may lead to erroneous geologic interpretations.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.328

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.030
GPT teacher head0.249
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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