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Record W3182161332 · doi:10.1080/08941920.2021.1936318

Salish Sea Survey: Geographic Literacy Enhancing Natural Resource Management

2021· article· en· W3182161332 on OpenAlexaboutno aff
David J. Trimbach, Joseph K. Gaydos, Kelly Biedenweg

Bibliographic record

VenueSociety & Natural Resources · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsnot available
FundersKaren C. Drayer Wildlife Health Center
KeywordsOutreachGeographyNatural resourceEnvironmental resource managementNatural resource managementLiteracyEcosystem managementResource management (computing)Resource (disambiguation)Geographic information systemEcosystemEcologyPolitical scienceBiologyCartography

Abstract

fetched live from OpenAlex

The Salish Sea is a biodiverse, transboundary inland sea and marine ecosystem stretching from British Columbia, Canada into Washington State, United States. Home to nearly eight million people and charismatic and keystone species, including three populations of orca, the ecosystem crosses multiple jurisdictions, communities, and watersheds, complicating conservation efforts. Geographic literacy, especially place names, is important for managing Salish Sea recovery and further challenged by the newness of the Salish Sea as an officially recognized place name. We conducted a geographic literacy survey showing that residents are largely unfamiliar with the name Salish Sea. Such low geographic literacy has numerous negative implications for communications, advocacy, outreach and the ability to address natural resource management and recovery of the Salish Sea at the level of the ecosystem. We offer potential implications for geographic literacy and other complementary geographic constructs within the wider field of natural 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 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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.002
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.279
Teacher spread0.269 · 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.

Study designNot applicable
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

Citations4
Published2021
Admission routes1
Has abstractyes

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