Maximizing Benefits From Punctual Ocean Infrastructure: An Ethical Perspective
Bibliographic record
Abstract
Ethics are becoming a component of best practices in ocean science and observing systems, with the research community facing a duty to society to maximize the efficient use and benefits that stem from investments in ocean science/monitoring. Sustained ocean observing systems on issues of global importance are coordinated, internationally sanctioned and making the most out of the resources accorded to them and consequently fulfilling their duty to society. However, globally huge investments are made annually in establishing infrastructure for shorter-term, punctual studies that address targeted as opposed to broad science needs. More could be done to maximize the benefits and impacts of these punctual efforts. Given punctual infrastructure’s small and frequently transient nature, connections to enable sharing will probably be done locally, and both potential additional users and owners of the infrastructure will need to be energetic, receptive and flexible. The accommodation of new uses will have to be balanced against any costs of these additional activities, which could pose an ethical dilemma in themselves if they compromise the infrastructure’s ability to meet its original intent. However, such adaptive infrastructures may be the most efficient way to provide the resources needed to identify and monitor emerging or new ocean stressors.
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 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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".