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Record W4414394394 · doi:10.1017/aaq.2025.25

Characterizing the Erosion of Coastal Archaeological Sites on the Maritime Peninsula Using Survey, Collection Analysis, Excavation, and Modeling

2025· article· en· W4414394394 on OpenAlexaffabout
Gabriel Hrynick, Arthur W. Anderson, Katelyn DeWater, William Kochtitzky, Arthur Spiess

Bibliographic record

VenueAmerican Antiquity · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicMaritime and Coastal Archaeology
Canadian institutionsUniversity of New Brunswick
FundersNational Geographic Society
KeywordsCoastal erosionPeninsulaArchaeological recordExtant taxonErosionNatural (archaeology)Nova scotiaProjectile point

Abstract

fetched live from OpenAlex

Abstract The erosion of coastal archaeological sites is a worldwide heritage crisis. However, regional variability in the archaeological record and the natural environment necessitates localized consideration of the erosion of archaeological sites to facilitate informed research prioritization decisions about coastal cultural resources. In this article, we present and compare the results of recent coastal survey programs from southern Nova Scotia and far northeastern Maine to earlier ones to ascertain the extent of erosion since the mid-twentieth century. We then situate regional erosion in culture-historical terms via a case study from archaeological sites at Sipp Bay, Maine, from which materials were collected and tested in the early to mid-twentieth century. We compare the results of that work to our recent excavations. Finally, we model future sea-level rise scenarios to estimate future site destruction and compare these models between regions. Together, these data illustrate patterns in site preservation for geoarchaeological examination, provide insight into erosion-driven biases in the extant archaeological record, and offer information to guide research prioritization.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.994

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 studies0.0010.002
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 head0.055
GPT teacher head0.281
Teacher spread0.226 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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