Quaternary environmental changes in tropical Lake Towuti, Indonesia, inferred from end‐member modelling of X‐ray fluorescence core‐scanning data
Bibliographic record
Abstract
ABSTRACT Continental and marine sediments are composed of a mixture from different sources and are influenced by a variety of environmental factors and transport processes prior to deposition. For analysis and interpretation, these sources and processes are often challenging to disentangle. We show that end‐member modelling of X‐ray fluorescence (XRF) core‐scanning data helps to overcome these challenges by unmixing different environmental signals from high‐resolution sediment geochemical records. We apply this approach to a 100 m long lacustrine succession from Lake Towuti, Indonesia, to separate the regional climate and tectonic history from local ecological and diagenetic processes. The resulting six end‐members (EMs) are interpreted to represent changes in ecological (EM1), climatic (EMs 2–4), tectonic (EM 5) and geomorphic (EM6) processes determining changes in sediment composition. Because end‐member analysis allows for the tracking of transient and overlapping processes, climatic changes can be followed throughout the 100 m‐long succession, suggesting alternating wet and dry periods in Central Sulawesi over long (several 100 000 years) time scales. We show that end‐member analysis on elemental data sets offers a detailed and objective means to disentangle depositional processes in sedimentary successions resulting from varying tectonic and environmental factors involved in sediment formation and deposition.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".