Deciphering long‐term records of natural variability and human impact as recorded in lake sediments: a palaeolimnological puzzle
Bibliographic record
Abstract
Global aquatic ecosystems are under increasing threat from anthropogenic activity, as well as being exposed to past (and projected) climate change, however, the nature of how climate and human impacts are recorded in lake sediments is often ambiguous. Natural and anthropogenic drivers can force a similar response in lake systems, yet the ability to attribute what change recorded in lake sediments is natural, from that which is anthropogenic, is increasingly important for understanding how lake systems have, and will continue to function when subjected to multiple stressors; an issue that is particularly acute when considering management options for aquatic ecosystems. The duration and timing of human impacts on lake systems varies geographically, with some regions of the world (such as Africa and South America) having a longer legacy of human impact than others (e.g., New Zealand). A wide array of techniques (biological, chemical, physical and statistical) is available to palaeolimnologists to allow the deciphering of complex sedimentary records. Lake sediments are an important archive of how drivers have changed through time, and how these impacts manifest in lake systems. With a paucity of ‘real‐time’ data pre‐dating human impact, palaeolimnological archives offer the only insight into both natural variability (i.e., that driven by climate and intrinsic lake processes) and the impact of people. While there is a need to acknowledge complexity, and temporal and spatial variability when deciphering change from sediment archives, a palaeolimnological approach is a powerful tool for better understanding and managing global aquatic resources. WIREs Water 2017, 4:e1195. doi: 10.1002/wat2.1195 This article is categorized under: Water and Life > Stresses and Pressures on Ecosystems Science of Water > Water and Environmental Change Water and Life > Methods
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".