ACCd and PGPRs: increasing plant productivity for more sustainable agricultural practices
Bibliographic record
Abstract
Our current world population is nearly 8 billion people, and it is expected to reach 10 billion by the year 2055 (Schultz, 2018). As the population continues to experience exponential growth patterns, we are faced with increasing concerns regarding food security. Indeed, as of 2020, millions of people are experiencing hunger or food insecurity, especially those in Africa, Latin America, the Caribbean, and Asia (FAO, 2021). A great deal of food insecurity is indirectly caused by climate change because product yield is not sufficient to reach increasing food demands (FAO, 2021). Climate change is causing warmer winters and unpredictable precipitation, resulting in poor growing conditions for crops (Wheaton & Kulshreshtha, 2017; Qian et al., 2011). In order to achieve high quality and quantity product yield, crops require rich soil conditions generated by sufficient periods of rainfall. This article discusses the effects of climate change on agriculture, with emphasis on aminocyclopropane-1-carboxylic acid deaminase (ACCd), an enzyme that plays an important role in increasing drought and salinity tolerance in plants.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".