A Response to Scientific and Societal Needs for Marine Biological Observations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Development of global ocean observing capacity for the biological EOVs is on the cusp of a step-change. Current capacity to automate data collection and processing and to integrate the resulting data streams with complementary data, openly available as FAIR data, is certain to dramatically increase the amount and quality of information and knowledge available to scientists and decision makers into the future. There is little doubt that scientists will continue to expand their understanding of what lives in the ocean, where it lives and how it is changing. However, whether this expanding information stream will inform policy and management or be incorporated into indicators for national reporting is more uncertain. Coordinated data collection including open sharing of data will help produce the consistent evidence-based messages that are valued by managers. The GOOS Biology and Ecosystems Panel is working with other global initiatives to assist this coordination by defining and implementing Essential Ocean Variables. The biological EOVs have been defined, are being updated following community feedback, and their implementation is underway. In 2019, the coverage and precision of a global ocean observing system capable of addressing key questions for the next decade will be quantified, and its potential to support the goals of the UN Decade of Ocean Science for Sustainable Development identified. Developing a global ocean observing system for biology and ecosystems requires parallel efforts in improving evidence-based monitoring of progress against international agreements and the open data, reporting and governance structures that would facilitate the uptake of improved information by decision makers.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it