Does Deficit Irrigation Affect the Relation between Radiation Interception and Water Consumption for Durum Wheat (Triticum durum Desf)?
Bibliographic record
Abstract
<p class="4Body">Total Dray Matter (TDM), Photosynthetically Active Radiation Intercepted (PARabs), Water Consumption (WC), Water use- (WUE), Radiation use efficiency (RUE) and the Relation between Radiation Interception and Water Consumption for Durum Wheat were investigate under different irrigation amount (D<sub>1</sub>= 100 % ETc; D<sub>2</sub>= 70 % ETc; D<sub>3</sub>= 40 % ETc and D<sub>4</sub>= pluvial) and during three growing seasons (2005-2006, 2006-2007 and 2007-2008). Results showed that, the cumulative PARabs decreased with deficit irrigation. In fact, D<sub>1</sub> treatment recorded the highest cumulative PAR abs and the lowest marked under D<sub>4</sub> treatment. Similarly, TDM and RUE were decreased with deficit irrigation. The highest RUE observed under the D<sub>1</sub> (from 1.32 to 1.43 g MJ<sup>-1</sup>) and the lowest under D<sub>4</sub> (from 1.17 to 1.29 g MJ<sup>-1</sup>). However WUE increased with deficit irrigation. The highest WUE were obtained under the D<sub>4</sub> (from 3 to 4 kg m<sup>-3</sup>) and the lowest were observed under D<sub>1</sub> (from 2.8 to 3.1 kg m<sup>-3</sup>). Significant linear relationship was found between cumulative PAR abs and cumulative water consumption with a high correlation coefficient (R<sup>2</sup>) only under the two treatments D<sub>1</sub> and D<sub>2</sub>.</p>
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 | 0.001 | 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 | 0.000 | 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 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".