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Record W2980575792 · doi:10.1093/plankt/fbz049

Biotic response to the environmental and climatic variability in a deep alpine lake (Lake Lugu) over the last 30 000 years in southwest China

2019· article· en· W2980575792 on OpenAlexaff
Qian Wang, Paul B. Hamilton, Giri Kattel, Lingyang Kong

Bibliographic record

VenueJournal of Plankton Research · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsCanadian Museum of Nature
FundersNational Natural Science Foundation of China
KeywordsSubfossilEcologyLake ecosystemEnvironmental scienceEcosystemLittoral zoneDiatomBosminaVegetation (pathology)Glacial periodPaleolimnologyClimate changePhysical geographyZooplanktonCladoceraOceanographyHoloceneGeographyGeologyBiology

Abstract

fetched live from OpenAlex

Abstract Multiple biogeochemical variables in a sediment core from Lake Lugu in southwest China were studied to investigate the effects of regional environmental changes on the ecosystem. Subfossil Cladocera, together with diatom, pollen and geochemical records, were used to examine climate-induced changes in lake ecosystem since the Last Glacial Maximum (30 000–0 cal year BP). Consistency among these biological records indicates that the succession of zooplankton, algae and vegetation changed in response to direct and indirect climatic factors. Alterations in the nutrient supply mediated by climate-induced changes in vegetation and soil processes are likely responsible for the variability of cladocerans. During the Last Glacial Maximum, cladocerans were dominated by littoral taxa (e.g. Alona), indicating an unproductive and oligotrophic lake system. A peak distribution in the pelagic Bosmina highlights the period of increased nutrient availability at 11 500 cal year BP. The ecological changes in Cladocera at 19 000 cal year BP and diatom communities at 18 000 cal year BP reveal independent and indirect responses to nutrient and light conditions induced by solar radiation and increased monsoon intensity across the study region. The palaeoecological archives from Lake Lugu sediments highlight a complex lake ecosystem influenced by both direct and indirect changes corresponding to climate changes and shifts in regional anthropogenic pressure over the last 30 000 years.

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)0.017
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.015
GPT teacher head0.270
Teacher spread0.255 · 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; a candidate call from one teacher head, not a consensus.

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

Citations7
Published2019
Admission routes1
Has abstractyes

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