Bibliographic record
Abstract
According to the requirement for major field-crops' growth and development on weather and climate conditions in Canada,principles of agricultural meteorology and climatology,as well as impacts and trends of global climate change on the crop production,six key agrometeorological factors(extreme air temperature,heavy precipitation,strong wind,freezing,extreme soil moisture and effective heat-energy conditions for crop production)were selected for development and evaluation of twelve critical agrometeorological indicators(cool spell and heat wave days,maximal daily and ten-day precipitations,maximal daily wind speed,strong wind days,frost-free and freezing days,standardized precipitation index,seasonal water deficit,effective growing degree days and crop cumulative heat units).They can be used for scientific regionalization and rational development of different types of field crops,including warm season crops and cool season crops of annual herbaceous species,and over-wintering crops of biennial and perennial herbaceous and woody species in various agricultural regions across the country.This study is also based on plant growth and development requirements for cardinal temperatures and water conditions for three types of major field crops and actual weather and climate characteristics during crop growing and overwintering seasons in any agricultural years to determine growing season starting and ending days of various field crops in Canada.The results can be used for rational arrangement,optimal selection and proper plantation of suitable field crops in various agricultural regions of the country,efficient farm operation and management,science advice,policy development and decision-making,as well as similar scientific researches in other countries and regions in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".