C&EN talks with Loreto Paulino, chemist and Arctic explorer
Bibliographic record
Abstract
In the summer of 2023, on the heels of graduating from the University of Guam with his bachelor’s in chemistry, Loreto Paulino Jr. set up camp in Alaska. He was there as part of the Polaris Project, which brings young scientists on climate change–related research expeditions in the Arctic. He and other project participants were temporarily stationed in the Yukon-Kuskokwim Delta, a vast tundra on the Bering Sea. This remote site lacks roads, so the crew assembled in the town of Bethel, Alaska, and traveled the 95 km to base camp by float plane. For the next 2 weeks, Paulino lived in a tent and made the daily trek to and from his study site—though he occasionally got a lift in a helicopter. Before the expedition embarked, program director Susan Natali mentioned that none of the Polaris Project students had studied beavers. Paulino jumped at the chance. Beavers have long
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.185 | 0.048 |
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".