Characterization of Multi-Nuclear Manganese-Binding Bacterial Reaction Centers from Rhodobacter sphaeroides
Bibliographic record
Abstract
abstract: In my thesis, I characterize multi-nuclear manganese cofactors in modified reaction \n\ncenters from the bacterium Rhodobacter sphaeroides. I characterized interactions \n\nbetween a variety of secondary electron donors and modified reaction centers. In Chapter \n\n1, I provide the research aims, background, and a summary of the chapters in my thesis. \n\nIn Chapter 2 and Chapter 3, I present my work with artificial four-helix bundles as \n\nsecondary electron donors to modified bacterial reaction centers. In Chapter 2, I \n\ncharacterize the binding and energetics of the P1 Mn-protein, as a secondary electron \n\ndonor to modified reaction centers. In Chapter 3, I present the activity of a suite of four\n\nhelix bundles behaving as secondary electron donors to modified reaction centers. In \n\nChapter 4, I characterize a suite of modified reaction centers designed to bind and oxidize \n\nmanganese. I present work that characterizes bound manganese oxides as secondary \n\nelectron donors to the oxidized bacteriochlorophyll dimer in modified reaction centers. In \n\nChapter 5, I present my conclusions with a short description of future work in \n\ncharacterizing multiple electron transfers from a multi-nuclear manganese cofactor in \n\nmodified reaction centers. To conclude, my thesis presents a characterization of a variety \n\nof secondary electron donors to modified reaction centers that establish the feasibility to \n\ncharacterize multiple turnovers from a multi-nuclear manganese cofactor.
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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) | 0.001 | 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 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".