Bibliographic record
Abstract
This painting is titled "French Quarter Musician" which I have completed as part of the Tolmas Scholars program for Privateer Undergraduate Research & Scholarly UNO Experience (PURSUE). My objectives for the PURSUE program were to paint a series of portraits that depict a sense of community in New Orleans while exploring different styles of painting. However, due to leaving New Orleans early into the program because of Covid-19, I worked from the few pictures I took of strangers on the street along with a portrait of an old friend attempting to capture their energy upon first meeting. First impressions tend to give a glimpse of the surrounding community. I met the "French Quarter Musician" as he was playing a bluesy melody, and when asked to take his picture and to stand however he wanted to, he began to play his trumpet again. In the painting, I chose an abstract approach based on the style of Jordan Casteel. While my painting does not resemble Casteel’s work a great deal, I followed a thought process of painting shadows and highlights in sections of solid color without blending, as her work tends to also do. I also used a color palette of warm deep colors ranging between purples and oranges while highlighting with a cool lavender hoping to capture the energy his bluesy melody was surrounding him with. I steered away from the main color palette when painting the trumpet to draw the eye near the center, leading to his face and hand.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.261 | 0.053 |
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".