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Record W4388197936 · doi:10.5194/cp-19-2027-2023

Disentangling environmental drivers of subarctic dinocyst assemblage compositional change during the Holocene

2023· article· en· W4388197936 on OpenAlexafffund
Sabrina Hohmann, Michal Kučera, Anne de Vernal

Bibliographic record

VenueClimate of the past · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaUniversität BremenDeutsche Forschungsgemeinschaft
KeywordsDinocystAssemblage (archaeology)HoloceneBayGeologyNorthern HemisphereTransectPhysical geographySubarctic climatePaleontologyOceanographyClimatologyEcologyGeographyBiologyPalynology

Abstract

fetched live from OpenAlex

Abstract. Analysis of compositional changes in fossil organic-walled dinoflagellate cyst (dinocysts) assemblages is a widely used tool for the quantitative reconstruction of past environmental parameters. This approach assumes that the assemblage composition is significantly related to the reconstructed environmental parameter, which requires an independent correlation between the assemblage and the variable, meaning that the variable explains a dimension of the assemblage variance that is not also explained by other parameters. Theoretically, dinocyst assemblages can be used to reconstruct multiple environmental variables. However, there is evidence that primary and subordinate drivers for assemblage compositions regionally differ, and it remains unclear whether a significant relationship to specific parameters in the present ocean always implies that this relationship is significant in fossil assemblages, questioning if past changes in these multiple parameters can be reconstructed simultaneously from fossil assemblages. Here, we analysed a local subset of the Northern Hemisphere dinocyst calibration dataset (n=1968), including samples from the Baffin Bay area (n=421), and evaluated the benefits of a local versus a more regional or global calibration for the environmental reconstruction of Baffin Bay oceanography during the Holocene. We determined the dimensionality of the dinocyst ecological response and identified environmental drivers in the Baffin Bay area for the modern dataset. We analysed four existing Holocene records along a north–south transect in the area and evaluated the statistical significance of downcore reconstructions by applying the local and global datasets with different techniques: the modern analogue technique (MAT), the weighted average partial least square (WA-PLS) and maximum likelihood (ML). We evaluated reconstructions tested as significant in light of the existing state of knowledge about West Greenland's Holocene palaeoceanography. Our analyses imply that present-day and Holocene dinocyst assemblages in the Baffin Bay area are primarily driven by salinity changes; other parameters were less important in driving assemblage compositions, and their contribution differed among the studied records. We show that the objectively occurring and temporally coherent shifts in dinocyst assemblages in the Holocene of Baffin Bay can be robustly interpreted only by transfer functions that are locally calibrated. Transfer functions based on the broad Northern Hemisphere calibration yielded many insignificant environmental reconstructions. At the same time, we show that even in the local calibration, not all parameters that appear to significantly affect dinocyst assemblages in the calibration dataset can be meaningfully reconstructed in the fossil record. A thorough evaluation of the calibration dataset and the significance of downcore applications is necessary to reveal the region-specific information contained in dinocyst assemblage composition.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.233
Teacher spread0.210 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2023
Admission routes2
Has abstractyes

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