A chironomid-based Holocene summer air temperature reconstruction from the Swiss Alps
Bibliographic record
Abstract
We developed a quantitative chironomid-July air temperature inference model based on surface sediments from 81 Swiss lakes and applied it to the Holocene subfossil chironomid record of Hinterburgsee, a small subalpine lake in the northern Swiss Alps (present-day mean July air temperature of 11.3°C). After smoothing to reduce the high between-sample variability of inferred temperatures, the reconstruction indicates July air temperatures of 10.4– 10.9°C at the end of the Younger Dryas, of 11.9–12.8°C during the early and mid-Holocene (11500–4000 cal. BP), and slightly lower temperatures of 11.5–12.0°C during the late Holocene (3500–1000 cal. BP). A warming trend inferred for the past millennium is most likely an artifact of human impact on Hinterburgsee's chironomid fauna, rather than a genuine temperature signal. The most prominent climatological events during the Holocene were two periods of lower temperatures at c. 10700–10500 cal. BP and 8200–7700 cal. BP and an abrupt shift to a cooler late-Holocene climate around 4000–3700 cal. BP. Although the chironomid-inferred climate signals were within the prediction error of the model (1.51°C), major inferred temperature changes agree well with other northern and central European climate reconstructions and underline the potential of subfossil chironomid analysis to reconstruct even the moderate climatic changes within the Holocene.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".