Statistical Analysis of Cropland Area in Canada using the Autoregressive Hidden Markov Time Series Model
Bibliographic record
Abstract
Crop production and other agricultural activities are as old as human existence and becoming increasingly intensive, spatially concentrated and specialized. However, diversification in economic activities and recent development in technology in many developed countries have led to significant increase in land use. Thereby, resulting to huge reduction in the total land area available for agricultural activities especially crop production. This study examines the distribution of cropland area in Canada in relation to three contributing factors using the Autoregressive Hidden Markov time series Model (AR-HMM) due to the limitations of the ordinary Autoregressive model in the accuracy of its parameter estimation. Expectation-Maximization (E-M) algorithm method was used to estimate the model parameters so as to investigate the effects of the factors on cropland distribution using secondary data from Food and Agriculture Organisation (FAO). Jarque-Bera and D'Agostino normality tests were carried out to examine the normality of the series. Augmented Dickey Fuller (ADF) and the KPSS tests established the stationarity of the series. The ideal stationary probability distribution for transition was at AR (3)-HMM with the minimum Bayesian Information Criterion (BIC) of 16270.62. The prior transition states for the HMM are 0.462, 0.260 and 0.278 respectively. In conclusion, this study suggests that deforestation and other land use activities as a result of commercial and technological advancements should be minimized to ensure more available cropland area.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".