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Record W3048851675 · doi:10.1017/aaq.2020.47

Spatiotemporal Variation in ΔR on the West Coast of North America in the Late Holocene: Implications for Dating the Shells of Marine Mollusks

2020· article· en· W3048851675 on OpenAlexaff
Ian Hutchinson

Bibliographic record

VenueAmerican Antiquity · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsHutchinson (Canada)
Fundersnot available
KeywordsHoloceneRadiocarbon datingDownwellingOceanographyGeologyUpwellingShoreLatitudePeriod (music)Longshore driftCenozoicPhysical geographyClimatologyGeographyPaleontologySedimentSediment transport

Abstract

fetched live from OpenAlex

Radiocarbon ages on mollusk shells, which account for about half of the more than 8,000 dates from cultural deposits on the west coast of North America, need to be corrected for the local marine reservoir effect (ΔR) to yield true ages. Assays on “prebomb” shells show that ΔR increases poleward, echoing the age gradient in offshore waters. The meridional gradient in ΔR is not appreciably affected by the transition either from an upwelling regime to a downwelling regime north of 40°N–45°N or from a winter maximum-high alkalinity river discharge pattern to a summer maximum-low alkalinity pattern at the same latitude, probably because these changes are offset by increasing storminess and tidal energy in coastal areas. Mesoscale variations in ΔR along this gradient are attributable to contrasts in shore morphology and exposure. Data from 123 shell-wood pairs reveal similar patterns of temporal variation in ΔR in the late Holocene in the coastal ecoregions. The characteristic temporal pattern echoes phases of variable El Niño-Southern Oscillation (ENSO) activity. The high degree of variability in ΔR argues against the indiscriminate application of regionally uniform or trans-Holocene ΔR values and demands improvements in spatiotemporal resolution if shell is used to date cultural deposits.

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 score0.019
Threshold uncertainty score1.000

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 studies0.0000.001
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 head0.038
GPT teacher head0.281
Teacher spread0.242 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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