Bibliographic record
Abstract
In order to address a paucity of information, a study was conducted in the Peace River region of northwestern Canada to determine the effects on seed production of creeping red fescue (Festuca rubra L. var. rubra) of six water treatments [natural precipitation and 100, 125, 150, 175 and 200% of the average, cumulative, long-term precipitation (LTP) of 459 mm yr-1] in combination with four N-fertilization regimes (zero + 102, 34 + 68, 68 + 34, and 102 + zero kg ha-1 N for the first + second crops, respectively). The 2-yr seed yield exceeded 2900 kg ha-1 when N was supplied to just one of the two seed crops, viz. with 150 to 200% LTP when N was applied only to the first seed crop, and with 125 to 150% LTP when N was applied only to the second seed crop. Under the prevailing, long-term, moisture environment at the study site (100% LTP), total seed yield over two production years was greatest (2136 kg ha-1) with 68 + 34 kg ha-1 N. The effects of the water and N treatments on seed yield were not associated with the mass of root organic matter recovered after the harvest of the second seed crop. These results confirm, and begin to quantify, numerous visual observations in the Peace River region that seed yields of creeping red fescue are restricted by insufficient precipitation. Key words: Creeping red fescue, Festuca rubra, grass seed production, nitrogen fertility, water, 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 | 0.000 |
| 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 | 0.000 | 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 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".