Supplementary material to "Sediment and carbon accumulation in a glacial lake in Chukotka (ArcticSiberia) during the late Pleistocene and Holocene: Combining hydroacoustic profiling and down-core analyses"
Bibliographic record
Abstract
Retrieval of parameters from supraregional studies for comparison:Information regarding sediment volumes, carbon pools and carbon accumulation rates were, where possible, extracted from literature sources in order to permit the supraregional comparison within this study.Extensive data for 31 Finnish lakes (sediment volumes, carbon pools and carbon accumulation rates) were retrieved from Pajunen (2000).Carbon accumulation rate data were extracted from Sobek et al., 2014 and Anderson et al., 2009 for 14 Greenlandic lake sites.10 lake sites from Quebec, Canada were included from Ferland et al., 2012 where hydroacoustically derived sediment volume and carbon accumulation rates were available.Sediment volumes and carbon storage for 11 Alberta, Canada lakes were extracted from Campbell et al., 2000 alongside the carbon accumulation rate which was not provided for each individual lake location but as a mean across 191 lake basins.The global lake and reservoir dataset of Mendonça et al., 2017 was utilized to obtain carbon accumulation rates for 343 global lake sites (excluding reservoirs and wetlands not relevant for comparison within this study).Carbon accumulation rates for five locations at lake Baikal were obtained from Sobek et al., 2014 that were originally provided by Martin et al., 1998.Carbon accumulation rates were also derived from a study of 20 Siberian thermokarst lakes published in Anthony et al., 2014.For Uinta lake sites, sediment volume was estimated by combining lake surface areas and maximum core depths from provided supplementary data.The carbon amount of each Uinta lake site was also extracted from supplementary data.Sediment volumes and carbon pools were acquired from Thermokarst lakes, lagoons and Yedoma deposits from multiple studies from Alaska and Siberia (Jenrich et al., in review; Jongejans et al., 2018; Windirsch et al., 2020)
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.583 | 0.123 |
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".