Long‐term perspectives on terrestrial and aquatic carbon cycling from palaeolimnology
Bibliographic record
Abstract
Lakes are active processors and collectors of carbon (C) and thus recognized as quantitatively important within the terrestrial C cycle. Better integration of palaeolimnology (lake sediment core analyses) with limnological C budgeting approaches has the potential to enhance understanding of lacustrine C processing and sequestration. Palaeolimnology simultaneously assimilates materials from across lake habitats, terrestrial watersheds, and airsheds to provide a uniquely broad overview of the terrestrial‐atmospheric‐aquatic linkages across different spatial scales. The examination of past changes over decadal–millennial timescales via palaeolimnology can inform understanding and prediction of future changes in C cycling. With a particular, but not exclusive, focus on northern latitudes we examine the methodological approaches of palaeolimnology, focusing on how relatively standard and well‐tested techniques might be applied to address questions of relevance to the C cycle. We consider how palaeolimnology, limnology, and sedimentation studies might be linked to provide more quantitative and holistic estimates of lake C cycling and budgets. Finally, we use palaeolimnological examples to consider how changes such as terrestrial vegetation shifts, permafrost thaw, the formation of new lakes and reservoirs, hydrological modification of inorganic C processing, land use change, soil erosion and disruption to global nitrogen and phosphorus cycles might influence lake C cycling. WIREs Water 2016, 3:211–234. doi: 10.1002/wat2.1130 This article is categorized under: Water and Life > Nature of Freshwater Ecosystems Science of Water > Water and Environmental Change Science of Water > Water Quality
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".