<scp>ASLO'</scp>s Global Outreach Initiative: Advancing World‐Wide Public Awareness of the Aquatic Sciences
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.384 | 0.117 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".