Using micro-computed tomography (µCT) to measure annually resolved sediment fluxes in varved sediments
Bibliographic record
Abstract
Annually laminated sediments (varves) are excellent paleoclimate archives because of their high temporal resolution and because they contain their own chronology that can be converted to calendar years. However, their studies can be tedious because they can be disturbed, irregular or very thin, sometimes less than a mm. Here we explore the potential of micro-computed tomography (µCT) to unlock paleoclimate signals from varved records.First, we show the added value of performing varve counts and thickness measurements from a 3D volume instead of a randomly sampled single 2D plane that is commonly used.Second, we investigate the possibility to obtain density measurements for each varve, allowing calculating annually resolved sediment fluxes in gr cm-2 a-1. To achieve this, we µCT-scanned several varved sequences containing sediments covering a wide range of density and extracted their linear attenuation. Then, discrete volumetric samples were subsampled, weighted, gradually dried every 30 minutes and µCT-scanned each time at two different incident energies. This allowed establishing a calibration of water content based on linear attenuation coefficients. Once the density and water content were obtained, the next step was to calculate the density and the mass accumulation rate for each varve.This approach will pave the way for “forward modelling,” namely the modelling of climate indicators (proxy) contained in natural archives. This method is recognized as a way of improving paleoclimate reconstructions, as it is less sensitive to the non-linearity of the physical processes involved in the formation of proxies than the statistical models that are commonly used.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".