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Record W4318218495 · doi:10.1002/lob.10552

<scp>ASLO'</scp>s Global Outreach Initiative: Advancing World‐Wide Public Awareness of the Aquatic Sciences

2023· article· en· W4318218495 on OpenAlexaboutno aff
Jessica Bellworthy

Bibliographic record

VenueLimnology and Oceanography Bulletin · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsnot available
Fundersnot available
KeywordsOutreachCitationLibrary sciencePolitical scienceMedia studiesSociologyComputer scienceLaw

Abstract

fetched live from OpenAlex

The ASLO Global Outreach Initiative (GOI) provides funding to members outside of the U.S. to conduct public education programs in the sciences of limnology and oceanography. The initiative is now in its fifth round of funding, with previous awards given in 2012 (reports of which are in L&O Bulletin Vol. 22, Issue 4), 2016 (Bulletin Vol. 26, Issue 3), 2017 (Bulletin Vol. 28, Issue 1), and 2019 for projects in 15 different countries. The GOI aims to make aquatic science topics more accessible and engaging to non-technical audiences, such as school children and members of local communities. The GOI fulfills ASLO's mission to foster a diverse, international scientific community that creates, integrates and communicates knowledge across the full spectrum of aquatic sciences, advance public awareness and education about aquatic resources and research, and promote scientific stewardship of aquatic resources for the public interest. New in 2021, the ASLO Board approved additional funds to sponsor an additional category of projects aimed at engaging Tribal and First Nations communities within North America. In total, $12,000 was allocated between five well-designed projects in Puerto Rico, New Zealand, Ecuador, Ghana, and First Nations communities in British Columbia, Canada. Project titles and locations are represented in Fig. 1. Reports written by the principal investigators appear alongside project photos within this and the following (May) issue of the Bulletin. Some projects faced setbacks imposed by remaining COVID-19 restrictions. These projects have provided an interim update as they continue into 2023; keep an eye on future Bulletin issues for the full reports. Congratulations to all 2021 awardees for the on-going success of your projects. If you have a creative idea for an impactful outreach project, applications for the next round of GOI funding will open in Spring 2023. Keep an eye out for more information! We would like to thank those ASLO members who have generously donated so we may fund more projects. If you are interested in donating to this program, you may do so at: http://bit.ly/2DCIzlA. All contributions received will be in addition to funds already committed by ASLO.

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 score0.118
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.002
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 head0.016
GPT teacher head0.234
Teacher spread0.218 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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