Extremely large SRA: fast and efficient algorithms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Implant-related infections or inflammation are one of the main reasons for implant failure. Therefore, different concepts for prevention are needed, which strongly promote the development and validation of improved material designs. Besides modifying the implant surface by, for example, antibacterial coatings (also implying drugs) for deterring or eliminating harmful bacteria, it is a highly promising strategy to prevent such implant infections by antibacterial substrate materials. In this work, the inherent antibacterial behavior of the as-cast biodegradable Fe69Mn30C1 (FeMnC) alloy against Gram-negative <i>Pseudomonas aeruginosa</i> and <i>Escherichia coli</i> as well as Gram-positive <i>Staphylococcus aureus</i> is presented for the first time in comparison to the clinically applied, corrosion-resistant AISI 316L stainless steel. In the second step, 3.5 wt % Cu was added to the FeMnC reference alloy, and the microbial corrosion as well as the proliferation of the investigated bacterial strains is further strongly influenced. This leads for instance to enhanced antibacterial activity of the Cu-modified FeMnC-based alloy against the very aggressive, wild-type bacteria <i>P. aeruginosa</i>. For clarification of the bacterial test results, additional analyses were applied regarding the microstructure and elemental distribution as well as the initial corrosion behavior of the alloys. This was electrochemically investigated by a potentiodynamic polarization test. The initial degraded surface after immersion were analyzed by glow discharge optical emission spectrometry and transmission electron microscopy combined with energy-dispersive X-ray analysis, revealing an increase of degradation due to Cu alloying. Due to their antibacterial behavior, both investigated FeMnC-based alloys in this study are attractive as a temporary implant material.
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.
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 | 0.000 | 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 it