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Record W3204537598 · doi:10.1029/2021av000456

Seasonality and Life History Complexity Determine Vulnerability of Dungeness Crab to Multiple Climate Stressors

2021· article· en· W3204537598 on OpenAlexaff
Halle M. Berger, Samantha Siedlecki, Catherine M. Matassa, Simone R. Alin, Isaac C. Kaplan, Emma E. Hodgson, Darren Pilcher, Emily Norton, Jan Newton

Bibliographic record

VenueAGU Advances · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsSimon Fraser University
FundersNOAA ResearchNational Oceanic and Atmospheric AdministrationUniversity of Connecticut
KeywordsVulnerability (computing)Pelagic zoneClimate changePopulationEcologyGeographyBiologyFisheryDemography

Abstract

fetched live from OpenAlex

Abstract Scaling climate change impacts from individual responses to population‐level vulnerability is a pressing challenge for scientists and society. We assessed vulnerability of the most valuable fished species in the Northwest U.S.—Dungeness crab—to climate stressors using a novel combination of ocean, population, and larval transport models with stage‐specific consequences of ocean acidification, hypoxia, and warming. Integration across pelagic and benthic life stages revealed increased population‐level vulnerability to each stressor by 2100 under RCP 8.5. Under future conditions, chronic vulnerability to low pH emerged year‐round for all life stages, whereas vulnerability to low oxygen continued to be acute, developing seasonally and impacting adults, which are critical to population growth. Our results demonstrate how ontogenetic habitat shifts and seasonal ocean conditions interactively impact population‐level vulnerability. Because most valuable U.S. fisheries rely on species with complex life cycles in seasonal seas, chronic and acute perspectives are necessary to assess population‐level vulnerability to climate change.

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)0.001
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 score0.033
Threshold uncertainty score0.656

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.048
GPT teacher head0.266
Teacher spread0.218 · 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

Citations32
Published2021
Admission routes1
Has abstractyes

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