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Record W4214894821 · doi:10.1080/15564894.2021.1991055

Contextualizing the influence of climate and culture on bivalve populations: <i>Donax obesulus</i> malacology from the north coast of Peru

2022· article· en· W4214894821 on OpenAlexaff
Jacob Warner, Aleksa K. Alaica

Bibliographic record

VenueThe Journal of Island and Coastal Archaeology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversity of TorontoUniversity of Alberta
FundersNational Science Foundation
KeywordsHolocenePopulationRadiocarbon datingOceanographyHorizonBivalviaClimate changeGeologyGeographyPaleontologyMolluscaPhysics

Abstract

fetched live from OpenAlex

Climate (in)stability can manifest in the size of mollusks attesting to variable impacts on growth, seasonal exploitation, and cultural persistence. We present population statistics of the height of a bivalve species (Donax obesulus) collected from sites dating to the Early Horizon (EH, 900–200 BCE) in the Nepeña Valley and the Middle Horizon (MH, 600–1000 CE) in the Jequetepeque Valley of northern coastal Peru and compare them with a paleoclimate record and a sample of modern shells (collected in 2012, 2014, and 2016) from the Nepeña Valley. We observe diachronic variation in the size of D. obesulus with larger bivalves during the EH and smaller shells during the MH and in the modern sample. D. obesulus size remains relatively static during the MH at one site through two sub-phases. These bivalve populations were likely impacted by both climate and cultural circumstances. A proxy for runoff from El Niño related rainfall (%lithic flux) from a previously published nearby marine sediment core is elevated during the EH and Late Moche phase of the MH and correlates with shell height. During the Transitional phase of the MH and the modern interval, however, there are periods of comparatively reduced El Niño activity and shell height compared to the EH.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.008
GPT teacher head0.223
Teacher spread0.215 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

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