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Record W2237073936

Science, Policy and Partnerships

2014· article· en· W2237073936 on OpenAlexaboutno aff
Katharine F. Wellman, Joel E. Baker

Bibliographic record

VenueWestern CEDAR (Western Washington University) · 2014
Typearticle
Languageen
FieldMedicine
TopicScience, Research, and Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Successful recovery of the Salish Sea requires collaboration between scientists (both biophysical and social) and policy/decision makers. Without this relationship we find ourselves with scientific research that is not relevant to decision making and decisions made without a strong scientific foundation, and without the support of the science community. While there is a great deal of good scientific work currently available to enhance ecosystem recovery decision-making, many of the questions that decision makers currently face require further investigation to address critical uncertainties, or at minimum, collection of data through environmental monitoring or social surveys to fill important gaps. However, it is critical that science does not impede early ecosystem-scale recovery actions; we do have sufficient knowledge to take action. In this panel session we will discuss the relationships between science and policy communities in ecosystem recovery efforts in the Salish Sea, including the science foundation for early action and the science and policy knowledge gaps for recovery at the scale of the Salish Sea. Panel members will include both US and Canadian professionals involved in the science and policy of ecosystem-based management for the Salish Sea. Panelists will consider and discuss such topics as:• An overview of Salish Sea status and trends and what they are really telling us and how should the status and trends influence policy and science• Examples of successes and challenges in science/policy collaboration. • Social constructs and behaviors needed for successful ecosystem recovery• Identified opportunities, specific approaches, and current challenges for science to more effectively inform policy decision-making. Panelists:Katharine Wellman –Moderator, Vice Chair Puget Sound Partnership Science Panel John Stein, Chair Puget Sound Partnership Science Panel Joel Baker, Puget Sound Institute, University of Washington Angela Bonifaci, US Environmental Protection Agency Tracy Collier, Puget Sound Partnership Thomas Leschine, School of Marine and Environmental Affairs, University of WashingtonIan Perry, Department of Fish and Oceans, Canada Terre Satterfield, University of British Columbia

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.809

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.048
GPT teacher head0.304
Teacher spread0.256 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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