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Record W2980798777 · doi:10.1073/pnas.1905921116

11,500 y of human–clam relationships provide long-term context for intertidal management in the Salish Sea, British Columbia

2019· article· en· W2980798777 on OpenAlexafffundabout
Ginevra Toniello, Dana Lepofsky, Gavia Lertzman‐Lepofsky, Anne K. Salomon, Kirsten Rowell

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2019
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsTula FoundationSimon Fraser University
FundersHakai Institute
KeywordsOverexploitationMaricultureIntertidal zoneEcosystemIndigenousContext (archaeology)GeographyMarine ecosystemEcologyHabitatFisherySustainabilityProductivityEnvironmental resource managementBiologyAquacultureEnvironmental scienceArchaeologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

) throughout the Holocene in the northern Salish Sea of British Columbia, Canada. We compare butter clam size and growth patterns from different temporal, environmental, and cultural contexts spanning 11,500 y to present. Butter clam size and growth were restricted in early postglacial times but increased over the next few millennia. During the early-Late Holocene, humans took increasing advantage of robust clam populations and after 3.5 ka, began constructing clam gardens (intertidal rock-walled terraces). Environmental and cultural variables, including coarse substrate, stabilized sea surface temperature, and the presence of a clam garden wall, increased clam growth throughout the Holocene. Measurements of clams collected in active clam gardens and deposited in middens suggest that clam gardens as well as other mariculture activities enhanced clam production despite increased harvesting pressure. Since European contact, decline of traditional management practices and increases in industrial activities are associated with reduced clam size and growth similar to those of the early postglacial clams. Deeper-time baselines that more accurately represent clam population variability and allow us to assess magnitudes of change throughout time as well as the complex interactions among humans and clams are useful for modern marine resource management.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.050
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.093
GPT teacher head0.386
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), 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

Citations86
Published2019
Admission routes3
Has abstractyes

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