A study of the heat budget components for the British Columbia and S.E. Alaska coast
Bibliographic record
Abstract
Knowledge of the surface heat transfer in coastal inlets would permit studies of their thermal structure and circulation. An assessment is made of data available for calculating the surface heat transfer for the coastal regions of British Columbia and S.E. Alaska. Monthly means of meteorological and oceanographic observations for the years 1961 and 1963 are critically examined for their representativness of conditions that exist over the open water. The location of the observation point is found to be important in choosing values for dew point and wind speed. Formulae for calculating surface heat transfer are examined for their potential applicability to a coastal climate. The calculated net annual surface heat transfer is found to be highest in the southern regions, approximately 90 langleys/day in the Strait of Georgia, and to decrease for more northerly regions, to an approximate balance with no net input in northern Chatham Strait. The annual cycle is found to be strongly modified by fine structure, the radiation balance dominating in summer, the convective losses in winter. Comparison of the calculated surface heat transfer with heat storage indicates that the calculations may be accurate to within 20% of the peak values. The range and shape of the surface temperature cycle was found to reflect the influence of advection, and deep water temperature, as well as the surface heat transfer.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".