Bibliographic record
Abstract
I wanted to create the garment to show a correlation between sophistication and a younger woman. I have been told that my style is mature for my age; therefore, I wanted to create a garment that portrayed that. By having the cranberry fabric backing the lace, it allows depth and contrast in comparison to the leather skirt. These characteristics help to define the meaning of being sophisticated and feminine while still having a Victorian yet vintage twist. Process: The process to create this two-piece garment started with an inspiration of linen and lace, which is my favorite texture and style of clothing. I have also learned numerous different dying techniques so I wanted to incorporate those into my designs as well. By using coffee and tea, I could dye my lace to give it a vintage look and portray a softer and more elegant color scheme. When it came to the skirt, I wanted to keep it simple yet intriguing, by using pleather, depth is added to create flow and femininity. The quarter sleeve crop-top mixed with the pleather a-line skirt puts a modern spin on a Victorian influenced garment. Techniques: draping, patternmaking, hand stitching, and fabric dying. Materials: 100-percent organic white cotton, 100-percent organic cranberry died cotton, pleather with a cotton backing, invisible zipper, hook and eye, 100-percent cotton thread. For her design, Krissy Knutz received the Fashion Show Best Design Award for 2014.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.817 | 0.555 |
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".