Interactions of climate and land use documented in the varved sediments of Seebergsee in the Swiss Alps
Bibliographic record
Abstract
This paper presents a multiproxy high-resolution study of the past 2600 years for Seebergsee, a small Swiss lake with varved sediments at the present tree-line ecotone. The laminae were identified as varves by a numerical analysis of diatom counts in the thin-sections. The hypothesis of two diatom blooms per year was corroborated by the 210Pb and 137Cs chronology. A period of intensive pasturing during the ‘Little Ice Age’ between ad 1346 and ad 1595 is suggested by coprophilous fungal spores, as well as by pollen indicators of grazing, by the diatom-inferred total phosphorus, by geochemistry and by documentary data. The subsequent re-oligotrophication of the lake took about 88 years, as determined by the timelag between the decline of coprophile fungal spores and the restoration of pre-eutrophic nutrient conditions. According to previous studies of latewood densities from the same region, cold summers around ad 1600 limited the pasturing at this altitude. This demonstrated the socio-economic impact of a single climatic event. However, the variance partitioning between the effects of land use and climate, which was applied for the whole core, revealed that climate independent of land use and time explained only 1.32% of the diatom data, while land use independent of climate and time explained 15.7%. Clearly land use in‘ uenced the lake, but land use was not always driven by climate. Other factors beside climate, such as politics or the introduction of fertilizers in the seventeenth and eighteenth centuries also in‘ uenced the development of Alpine pasturing.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".