Real differences – A lesson from an agronomist's perspective
Bibliographic record
Abstract
Karamanos, R., Flaten, D. N. and Stevenson, F. C. 2014. Real differences – Lessons from an agronomist's perspective. Can. J. Plant Sci. 94: 433–437. An experiment including a two penny treatment and an untreated check was established to show that implausible differences can be a statistical reality. An individual crop by location analysis showed that two pennies significantly (P<0.05) increased canola yield at one of the 19 locations, nearly (0.05<P<0.18) affected canola yield at one other location. A combined mixed model analysis showed that canola yield significantly increased by a small amount (0.1 t ha −1 ) with two pennies, whereas, a similar mixed model that accounted for residual variance heterogeneity showed that two pennies did not affect crop yield. Our results confirmed that the effect of a treatment not expected to cause a meaningful difference can be detected The results also highlight the importance of modeling all sources of variance, designing more efficient experiments, scrutinizing the size of treatment differences, and choosing an appropriate level of significance to ensure that only real differences are detected.
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 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 | 0.000 | 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) | 0.001 | 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 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".