The use of climatic data to estimate irrigation water requirements in the south central interior of British Columbia
Bibliographic record
Abstract
Climatic data observed at six meteorological recording stations in the south central Interior of British Columbia were used to analyse the temporal and geographical variations in the frequency, intensity and duration of various climatic phenomena that affect the supply and demand of water by growing crops. Rather than using average values, the relative frequencies of occurrence of each element or combination of elements were examined, in order that a more objective picture of the range of conditions experienced within the region during the growing season might be obtained. An inspection of the moisture supply patterns indicated that greater amounts of precipitation tended to occur during the earlier half of the growing season at most stations, the month of June experiencing a definite maximum. However, natural precipitation would appear to be less effective for plant growth than its absolute totals suggest, due to its tendency to be concentrated into a few days per month. An analysis of the occurrence of wet and dry spells during the growing season using two probability models supported these facts, the highest frequency of wet spells occurring in June, while the lower probabilities of wet spells in July and August indicated an increase in the length of dry spells during the second half of the growing season. Although it is known that at least four weather elements affect water loss by crops (radiation, temperature, wind and water pressure deficit), daily data were only available for two of these elements, namely air temperature and relative humidity. An examination of the relative frequencies of their occurrence showed that the evaporative power of the air remained relatively low until the end of June, after which it increased sharply as these two elements combined in such a manner that they intensified evaporation loss. This fact was further illustrated when their joint daily observations were combined in a frequency table, both July and August experiencing the highest relative frequencies of torrid days (hot days with low relative humidities). The conclusions were further verified when the actual amounts of irrigation water were computed at selected stations by estimating potential evapotranspiration rates from Penman's empirical formula and using the soil budget technique. At all stations except Lytton, little irrigation was required in most years until the beginning of July, unless the soils had low moisture storage capacities, but from July to September the required irrigation amounts were considerably higher, a fact that was due to both the increased dryness of the atmosphere and to the previous depletion of the readily available soil moisture.
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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 | 0.002 |
| 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) | 0.001 | 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".