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Record W1986568358 · doi:10.1139/cjb-2012-0300

Sea change under climate change: case studies in rare plant conservation from the dynamic San Francisco Estuary

2013· article· en· W1986568358 on OpenAlexvenueno aff
Brenda J. Grewell, Erin K. Espeland, Peggy L. Fiedler

Bibliographic record

VenueBotany · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsnot available
FundersUniversity of California, DavisCalifornia Department of Parks and RecreationU.S. Fish and Wildlife ServiceU.S. Army Corps of EngineersSan Francisco State UniversityMassachusetts Department of Fish and Game
KeywordsBiologyBiological dispersalMetapopulationThreatened speciesEcologyHabitatPopulationClimate changePropagule

Abstract

fetched live from OpenAlex

We present case studies supporting management of two rare plant species in tidal wetlands of the San Francisco Estuary. We used empirical demographic analyses to identify factors to enhance population establishment and survival of Chloropyron molle subsp. molle (Orobancaceae), an annual hemiparasite, and to compare reintroduced with natural populations. Twelve years after outplanting, the reintroduced population persists but is in decline; impediments to success include the lack of adaptive management response to weed invasions and muted variance in hydrology. Transplantation of Lilaeopsis masonii (Apiaceae), a rhizomatous perennial herb, failed to meet success criteria for mitigation at local project scale, but dispersal and establishment of metapopulation patches indicated persistence at the landscape scale. This species has been found to be genetically indistinct from a widespread congener, and has few threats to persistence so long as suitable habitat is present. These two examples demonstrate the need for integrated conservation management strategies that prioritize habitat connectivity and maintain physical processes to support dispersal in response to sea level rise. For the hemiparasite, assisted colonization may sustain populations threatened by sea level rise, but only if a strong commitment to effective stewardship is realized.

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)0.000
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 score0.226
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.001

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 head0.041
GPT teacher head0.258
Teacher spread0.217 · 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

Citations11
Published2013
Admission routes1
Has abstractyes

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