MétaCan
Menu
← Back to cohort
Record W2194445028 · doi:10.1371/journal.pone.0144861

Evaluating Threats in Multinational Marine Ecosystems: A Coast Salish First Nations and Tribal Perspective

2015· article· en· W2194445028 on OpenAlexaboutno aff
Joseph K. Gaydos, Sofie Thixton, Jamie Donatuto

Bibliographic record

VenuePLoS ONE · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersKaren C. Drayer Wildlife Health CenterUniversity of California, DavisSchool of Veterinary Medicine, University of California, DavisU.S. Environmental Protection Agency
KeywordsMarine conservationNatural resourceEnvironmental resource managementGovernment (linguistics)GeographyMarine ecosystemHuman settlementEnvironmental planningEnvironmental protectionIndigenousMarine protected areaEcosystemBusinessEcologyEnvironmental scienceHabitat

Abstract

fetched live from OpenAlex

Despite the merit of managing natural resources on the scale of ecosystems, evaluating threats and managing risk in ecosystems that span multiple countries or jurisdictions can be challenging. This requires each government involved to consider actions in concert with actions being taken in other countries by co-managing entities. Multiple proposed fossil fuel-related and port development projects in the Salish Sea, a 16,925 km2 inland sea shared by Washington State (USA), British Columbia (Canada), and Indigenous Coast Salish governments, have the potential to increase marine vessel traffic and negatively impact natural resources. There is no legal mandate or management mechanism requiring a comprehensive review of the potential cumulative impacts of these development activities throughout the Salish Sea and across the international border. This project identifies ongoing and proposed energy-related development projects that will increase marine vessel traffic in the Salish Sea and evaluates the threats each project poses to natural resources important to the Coast Salish. While recognizing that Coast Salish traditions identify all species as important and connected, we used expert elicitation to identify 50 species upon which we could evaluate impact. These species were chosen because Coast Salish depend upon them heavily for harvest revenue or as a staple food source, they were particularly culturally or spiritually significant, or they were historically part of Coast Salish lifeways. We identified six development projects, each of which had three potential impacts (pressures) associated with increased marine vessel traffic: oil spill, vessel noise and vessel strike. Projects varied in their potential for localized impacts (pressures) including shoreline development, harbor oil spill, pipeline spill, coal dust accumulation and nearshore LNG explosion. Based on available published data, impact for each pressure/species interaction was rated as likely, possible or unlikely. Impacts are likely to occur in 23 to 28% of the possible pressure/species scenarios and are possible in another 15 to 28% additional pressure/species interactions. While it is not clear which impacts will be additive, synergistic, or potentially antagonistic, studies that manipulate multiple stressors in marine ecosystems suggest that threats associated with these six projects are likely to have an overall additive or even synergistic interaction and therefore impact species of major cultural importance to the Coast Salish, an important concept that would be lost by merely evaluating each project independently. Failure to address multiple impacts will affect the Coast Salish and the 7 million other people that also depend on this ecosystem. These findings show the value of evaluating multiple threats, and ultimately conducting risk assessments at the scale of ecosystems and highlight the serious need for managers of multinational ecosystems to actively collaborate on evaluating threats, assessing risk, and managing resources.

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)0.009
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.004
Science and technology studies0.0050.004
Scholarly communication0.0100.005
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.121
GPT teacher head0.310
Teacher spread0.190 · 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 designQualitative
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

Citations25
Published2015
Admission routes1
Has abstractyes

Explore more

Same venuePLoS ONE→Same topicMarine animal studies overview→French-language works237,207→