Comparing historical and modern methods of sea surface temperature measurement – Part 1: Review of methods, field comparisons and dataset adjustments
Bibliographic record
Abstract
Abstract. Sea Surface Temperature (SST) measurements have been obtained from a variety of different platforms, instruments and depths over the post-industrial period. Today most measurements come from ships, moored and drifting buoys and satellites. Shipboard methods include temperature measurement of seawater sampled by bucket and in engine cooling water intakes. Engine intake temperatures are generally thought to average a few tenths of a °C warmer than simultaneous bucket temperatures. Here I review SST measurement methods, studies comparing shipboard methods by field experiment and adjustments applied to SST datasets to account for variable methods. In opposition to contemporary thinking, I find average bucket-intake temperature differences reported from field studies inconclusive. Non-zero average differences often have associated standard deviations that are several times larger than the averages themselves. Further, average differences have been found to vary widely between ships and between cruises on the same ship. The cause of non-zero average differences is typically unclear given the general absence of additional temperature observations to those from buckets and engine intakes. Shipboard measurements appear of variable quality, highly dependent upon the accuracy and precision of the thermometer used and the care of the observer where manually read. Methods are generally poorly documented, with written instructions not necessarily reflecting actual practices of merchant mariners. Measurements cannot be expected to be of high quality where obtained by untrained sailors using thermometers of low accuracy and precision.
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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 | 0.120 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".