Air temperature data for the Labrador coast during the late 18th century
Bibliographic record
Abstract
The meteorological data for the Labrador coast in the late 18th century (1771–87). The oldest meteorological data for the area of the northeastern coast of the Labrador Peninsula come from the second half of the 18th century. Weather observations were carried out by members of the Moravian Church, who settled there in 1771 and founded the first Christian mission in a settlement called Nain. Within a few years, two more settlements were established: Okak (formerly known as Okkak) and Hopedale (formerly Hoffenthal). The data were taken from the meteorological registers available at three major archives: the Moravian Archives in Herrnhut (Germany), the Moravian Archives at Muswell Hill and the Archives of the Royal Society, both located in London (United Kingdom). Based on the available materials, three air temperature data series were developed, one for each station. Meteorological observations were carried out at Nain for the period Oct 1 1771 – 31 Jul 1786. During this time, there was one gap in measurements from 20 Oct 1774 to 16 Sep 1775 and from 12 to 30 Sep 1784. At Okak, observations were made from 16 Oct 1776 to 31 Aug 1786 without interruption. At Hopedale, observations were conducted between 1 Oct 1782 and 16 Aug 1786. This series is less complete because it has irregular gaps in observations: short gaps ranging from single observations in a day to a complete lack of observations for three days; and one long gap from 01 Aug 1784 to 31 Aug 1785.All available historical data have been quality-controlled and corrected. The air temperature was converted to degrees Celsius.During the study period, the Moravian missionaries made measurements in Nain at 08:00 and 14:00, but there were days with three, four or five measurements daily. Okak and Hopedale implemented a standardised measurement schedule, with readings consistently taken at 08:00, 12:00, 16:00 and 20:00.Mean daily air temperatures (MDAT) for the historical periods were calculated using nine different formulas ([2]–[10]) listed below. Using contemporary data from Nain and Hopedale from the period 1991–2000, we calculated MDAT also for all the formulas below (including formula [1]). Formula (1) uses 24 hourly observations per day, which allows us to calculate “real” MDAT. To identify biases in calculating MDATs using formulas (2)–(10), the differences between them and MDAT1 (formula [1]) were calculated separately for each month (see Table 2 in Singh et al. 2024). In this way, calculated corrections were added to historical MDAT2–MDAT10 values to obtain corrected values of MDAT for this period. For Okak (no instrumental data available at present), the corrections were taken from Nain. MDAT1= (T1+T2+T3+ … T24)/24 (1) MDAT2= (T8+T14)/2 (2) MDAT3 = (T8+T14+T21)/3 (3) MDAT4 = (T3+T8+T14+T16)/4 (4) MDAT5= (T4+T8+T12+T14+T16)/5 (5) MDAT6 = (T6+T8+T12+T14+T16)/5 (6) MDAT7 = (T6+T8+T12+T16+T20)/5 (7) MDAT8= (T7+T12+T16+T20)/4 (8) MDAT9= (T8+T12+T16+T20)/4 (9) MDAT10 = (T8+T12+T14+T16)/4 (10)
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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 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.007 | 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".