Time‐Lapse Cameras for Measurement of Grain Corn Phenology on the Canadian Prairies
Bibliographic record
Abstract
Core Ideas Time‐lapse cameras provided accurate grain corn development dates to V6 and for VT and R1. Grain corn hybrids all accumulated more corn heat unit (CHU) to R6 than indicated by their CHU ratings. The general thermal index may be more accurate than CHU for modeling grain corn phenology from temperature. Shorter season grain corn ( Zea mays L.) varieties and increased heat unit accumulation are improving grain corn production feasibility on the Canadian Prairies. However, there remains significant production risk and need for a reliable measure of corn heat requirements for risk assessment. This study utilized time‐lapse cameras to capture phenological development of five grain corn hybrids with corn heat unit (CHU) ratings from 2200 to 2700 at multiple locations in western Canada. The goal was to compare heat unit accumulation at specific phenological stages at multiple locations to identify an index with consistent accumulated values. The photos provided a clear record of development up to the V6 stage and for the VT and R1 stages. This facilitated accurate quantification of accumulated days after planting (DAP), CHU, standard growing degree day (GDD 10 ), modified growing degree day (mGDD 10 , 30 ), general thermal index (GTI), and beta function (BFn) for the five hybrids. All hybrids accumulated more CHU to reach R6 stage than suggested by their ratings. The coefficient of variation (CV) for accumulated index values was greatest at emergence and the V2 stage but declined in subsequent stages. At R6, all thermal indices had a CV ≤ 10.5%, with GTI consistently showing the numerically lowest CV (<5%) for all hybrids. The GTI may be a more consistent thermal index than CHU for predicting corn phenological development and an important improvement for selecting appropriate grain corn hybrids for specific locations on the Canadian Prairies. Further time‐lapse camera data is warranted to confirm these observations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".