Methods of sampling and analysis and our concepts of ocean dynamics
Bibliographic record
Abstract
In the last two decades, the increase in multi-nation, multi-ship projects has underlined the importance of intercalibration and intercomparison studies. At the same time, it is becoming obvious that with our present methods of sampling and analysis we can never hope to distinguish the variability in our sampling and analysis techniques from the variability in the universe sampled. The presence of discontinuities in many of the quantities measured suggests that our sampling grid, as maintained by oceanographic vessels, is too limited both in space and in time. Some variety of self-reporting sampling buoy is the obvious answer for physical and chemical parameters, although as yet we do not have detectors for many of the quantities of interest. Even with such buoys, it would seem unlikely that we could construct a grid of such buoys tight enough to measure the local variability of the oceans without the buoys themselves contributing to this variability. The limit finally selected for such a sampling grid will probably be some compromise between scientific capability and economic reality.
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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 | 0.060 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.036 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".