Why use theories in qualitative research?
Bibliographic record
Abstract
Abstract Phosphate acquisition by plants is an essential process that is directly implicated in the optimization of crop yields. Purple acid phosphatases (PAPs) are ubiquitous metalloenzymes, which catalyze the hydrolysis of a wide range of phosphate esters and anhydrides. While some plant PAPs display a preference for ATP as the substrate, others are efficient in hydrolyzing phytate or 2-phosphoenolpyruvate (PEP). PAP from red kidney bean (rkbPAP) is an efficient ATP- and ADPase, but has no activity towards phytate. The crystal structure of this enzyme in complex with an ATP analogue (to 2.20 Å resolution) provides insight into the amino acid residues that play an essential role in binding this substrate. Homology modelling was used to generate three-dimensional structures for the active sites of PAPs from tobacco (NtPAP) and Arabidopsis thaliana (AtPAP12 and AtPAP26) that are efficient in hydrolyzing phytate and PEP as substrates, respectively. In combination with substrate docking simulations and a phylogenetic analysis of 49 plant PAP sequences (including the first PAP sequences reported from Eucalyptus), several active site residues were identified that are important in defining the substrate specificities of plant PAPs. These results may inform bioengineering studies aimed at identifying and incorporating suitable plant PAP genes into crops to improve phosphorus use efficiency. Organic phosphorus sources increasingly supplement or replace inorganic fertilizer, and efficient phosphorus use of crops will lower the environmental footprint of agriculture while enhancing food production.
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 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 | 0.280 | 0.365 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.005 | 0.061 |
| Scholarly communication | 0.016 | 0.037 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".