First human impacts and responses of aquatic systems: A review of palaeolimnological records from around the world
Bibliographic record
Abstract
Lake sediments constitute natural archives of past environmental changes. Historically, research has focused mainly on generating regional climate records, but records of human impacts caused by land use and exploitation of freshwater resources are now attracting scientific and management interests. Long-term environmental records are useful to establish ecosystem reference conditions, enabling comparisons with current environments and potentially allowing future trajectories to be more tightly constrained. Here we review the timing and onset of human disturbance in and around inland water ecosystems as revealed through sedimentary archives from around the world. Palaeolimnology provides access to a wealth of information reflecting early human activities and their corresponding aquatic ecological shifts. First human impacts on aquatic systems and their watersheds are highly variable in time and space. Landscape disturbance often constitutes the first anthropogenic signal in palaeolimnological records. While the effects of humans at the landscape level are relatively easily demonstrated, the earliest signals of human-induced changes in the structure and functioning of aquatic ecosystems need very careful investigation using multiple proxies. Additional studies will improve our understanding of linkages between human settlements, their exploitation of land and water resources, and the downstream effects on continental waters.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".