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Resilience to Climate Change in Coastal Marine Ecosystems

2012· review· en· W2167328905 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnnual Review of Marine Science · 2012
Typereview
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEcosystemClimate changeEcologyAdaptive capacityEcological resilienceDisturbance (geology)Psychological resilienceEnvironmental resource managementResistance (ecology)Ecosystem diversityMarine ecosystemEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Ecological resilience to climate change is a combination of resistance to increasingly frequent and severe disturbances, capacity for recovery and self-organization, and ability to adapt to new conditions. Here, we focus on three broad categories of ecological properties that underlie resilience: diversity, connectivity, and adaptive capacity. Diversity increases the variety of responses to disturbance and the likelihood that species can compensate for one another. Connectivity among species, populations, and ecosystems enhances capacity for recovery by providing sources of propagules, nutrients, and biological legacies. Adaptive capacity includes a combination of phenotypic plasticity, species range shifts, and microevolution. We discuss empirical evidence for how these ecological and evolutionary mechanisms contribute to the resilience of coastal marine ecosystems following climate change-related disturbances, and how resource managers can apply this information to sustain these systems and the ecosystem services they provide.

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.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.944
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.004
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.016
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.033
GPT teacher head0.320
Teacher spread0.287 · 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