Inferring Past Trends in Lake Water Organic Carbon Concentrations in Northern Lakes Using Sediment Spectroscopy
Bibliographic record
Abstract
Changing lake water total organic carbon (TOC) concentrations are of concern for lake management because of corresponding effects on aquatic ecosystem functioning, drinking water resources and carbon cycling between land and sea. Understanding the importance of human activities on TOC changes requires knowledge of past concentrations; however, water-monitoring data are typically only available for the past few decades, if at all. Here, we present a universal model to infer past lake water TOC concentrations in northern lakes across Europe and North America that uses visible-near-infrared (VNIR) spectroscopy on lake sediments. In the orthogonal partial least-squares model, VNIR spectra of surface-sediment samples are calibrated against corresponding surface water TOC concentrations (0.5–41 mg L –1 ) from 345 Arctic to northern temperate lakes in Canada, Greenland, Sweden and Finland. Internal model-cross-validation resulted in a R 2 of 0.57 and a prediction error of 4.4 mg TOC L –1 . First applications to lakes in southern Ontario and Scotland, which are outside of the model’s geographic range, show the model accurately captures monitoring trends, and suggests that TOC dynamics during the 20th century at these sites were primarily driven by changes in atmospheric deposition. Our results demonstrate that the lake water TOC model has multiregional applications and is not biased by postdepositional diagenesis, allowing the identification of past TOC variations in northern lakes of Europe and North America over time scales of decades to millennia.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".