Bibliographic record
Abstract
As a learner, I’ve always been curious about the world around me, including a keen interest in how organisms function at a cellular level. This curiosity led to me developing a passion for the sciences. Not only that but as a curious individual I have always felt drawn to the sciences, a field where questions are always encouraged, hence the title of piece being “But, why?”. My passion for understanding the world at a cellular level made me want to study CPPS for my undergrad. Now, being in the second year of my degree I not only enjoy my classes but also appreciate the shift in perspective they have caused in how I view the world around me. This art piece is dedicated to the change in perspective I have experienced. Through my art piece, I try to use biological figures in imitation of the natural world to show that the things we learn are everywhere around us. For example, the DNA bridge is indicative of our genetic material being the backbone of who we are at the molecular level. Below the DNA bridge, I included the initials R.E.F as an homage to Rosalind E. Franklin whom I first learned about in grade 11 about not having received credit for her work in revealing the double helix formation of DNA. I drew the DNA so that it is unwinding closer to the end to show how there is still so much we have yet to discover and understand regarding its many complexities. I also included a body of water since it is crucial to many forms of life, and inside of it, I drew outlines of duplicating cells. Next to the water, is a phospholipid bilayer, something that has come up in my studies since high school as something simple yet crucial. The bacteriophages creeping towards the left are meant to contrast the bright and joyful imagery, to show how in sciences we learn about the interesting ways in which our bodies and environments are able to fend off potential dangers. However, these dangers are also important in the balance of life and the natural environment. The greenery framing the art piece is meant to represent the extracellular matrix, the various proteins that hold cells together. I tried to imitate this through my painting as crosslinked greenery. In the bottom right corner, I’ve also drawn myself looking up from reading a textbook as a way to show what it’s like to learn about such fascinating things and how it directly shifts my world view. Lastly, I drew this piece with a clear vanishing point as a metaphorical way of showing that our knowledge of the world and the study of science has gone through a journey and though our knowledge may have “dates of discovery” and points at which we began the studies of certain things, there is still no clear end to learning and everything we discover only leads to more questions which is definitely my favourite part about learning in my degree. Similar to the endless journey of the sciences, I look forward to where my own journey in this field will go.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.439 | 0.189 |
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".