Annually resolved grain-size distributions in varved sediments using image analysis - application to Paleoclimatology.
Bibliographic record
Abstract
Varved sediments are unique archives because they contain continuous and undisturbed records of past climatic \nconditions with an internal robust chronology. In many case, conceptual models for the varve formation can be \nestablished linking processes occurring in the watershed, such as river floods or snow melt, to specific lamina within \nthe varve structure. However, the physical properties of such layers, including grain-size, are seldom measured \ndespite their intrinsic value as indicators of hydrological processes. \nThis paper reviews the development and improvements of an image analysis methodology to extract grain-size \ndata from finely laminated sediments. The technique uses thin-sections from sediment cores, scanning electron \nmicroscope images of carefully selected regions of interest from the thin-sections, and an image analysis routine \nto extract semi-automatically grain-size data. \nAn example from Cape Bounty in the Canadian High Arctic is presented: grain-size data within each varve was \nmeasured for the last 2845 years. Several particle size distribution indices for each individual facies were calculated \nand combined to identify each type of sedimentary facies encountered within the sequence. For instance, high \nstandard deviation and 98th percentile index values are interpreted as high-energy events such as turbidites and \ndebris flows. \nMoreover, some grain-size indicators from the most recent varves correlate well with instrumental climate data. \nFor instance, the 98th percentile grain size has a strong correlation (R2=0.71) with summer rainfall. This kind of \nrelationship allows for the calibration of the image-analysis generated grain-size data set in terms of hydroclimatic \nparameters. The rainfall reconstruction suggests that Cape Bounty recently experienced an unprecedented increase \nsince ∼1920 AD. \nThese results contrast to other common varve measurements. For instance, varve thickness is not significantly \ncorrelated with the particle size distribution, and is poorly linked to the instrumental record. Indeed, sediment accumulation \ncan result from the accumulation of different successive hydroclimatic and geomorphic mechanisms \nsuch as spring snowmelt, rain events and landslides, as well as by changes in lake circulation and stratification. \nTherefore, a detailed grain-size obtained using image analysis appears to be a better approach to reconstruct past \nhydroclimatic conditions in this clastic sedimentary setting and holds tremendous potential to improve paleoclimatic \nreconstructions.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".