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Record W4408415374 · doi:10.1017/qua.2024.56

Chironomid-inferred postglacial temperature reconstruction from Gold Lake, Oregon, USA

2025· article· en· W4408415374 on OpenAlexaff
Jamila Baig, Daniel G. Gavin, Ian R. Walker, David F. Porinchu

Bibliographic record

VenueQuaternary Research · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of British Columbia, Okanagan Campus
Fundersnot available
KeywordsGeologyArchaeologyOceanographyPaleontologyGeography

Abstract

fetched live from OpenAlex

Abstract A paleotemperature reconstruction inferred from subfossil chironomid (non-biting midge) assemblages in a 13-meter, 14,500-yr lake sediment record from a montane forest in the Pacific Northwest is compared to existing quantitative temperature reconstructions from the Pacific Northwest. With updated temperatures, a regional training set was used to develop a midge-based mean July air temperature (MJAT) inference model (r 2 jack = 0.71, root mean square error of prediction = 1.09°C). The average inferred MJAT varied between 9.4°C and 13.2°C. During the late-glacial period, MJAT ranged between 9.4°C and 10.8°C, and the lowest MJAT (9.4°C) is inferred at ca. 12.7 ka during the Younger Dryas. The transition into the Early Holocene was marked by an increase from 11°C at 11 ka to 12°C at 9.2 ka. Following deposition of the Mazama tephra, chironomid concentration decreased rapidly, and MJAT rose to 12.3°C at ca. 7.6 ka. This change in chironomid assemblage may be due to the direct effects of the tephra on the surface energy balance. The reconstructed temperature did not track decreasing Holocene summer insolation but instead revealed Late Holocene warming, which is similar to a chironomid reconstruction in the eastern Sierra Nevada and a sea-surface temperature reconstruction from northern California.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.004

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.

Opus teacher head0.040
GPT teacher head0.317
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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