Once Upon a Time, in AmeriFlux
Bibliographic record
Abstract
Abstract In October 2020, under COVID‐19 quarantine, AmeriFlux held its largest and one of its most successful annual meetings. Historically, ∼100 scientists attend; this meeting had over 400 registrants and participants. Participants expressed that this was among the best virtual meetings that they had ever attended, and 100% of post‐meeting survey respondents stated that they would attend again. Feedback revealed the meeting fostered a strong sense of connection to the AmeriFlux community, especially among early career and international scientists. How did a feeling of strong connection to the community arise from the seemingly cold and isolated structures of virtual meetings? The meeting emphasized Diversity, Equity, and Inclusion (DEI), which resulted in an unexpected enhancement of science communication and community connections. Additionally, the meeting experimented with an online virtual gaming‐like world (Gather.town), where users controlled video/audio‐enabled avatars in a conference center and poster hall environment to create spontaneous conversations and discussions. In lieu of a social‐bonding field trip, participants showed videos of their field sites accompanied by informal banter, which were watched in group settings in Gather. Social mixers were structured over Zoom breakout rooms that were limited in size to promote participation with accessible games. Science talks were selected based on appeal to a demographically diverse organizing committee, which enhanced appeal to a broad meeting audience. Finally, breakout reports were given not in the format of bullet point slides, but instead as creatively improvized fairytales, which dramatically enhanced engagement. Here, we describe some of the process that went into the AmeriFlux 2020 meeting. Keeping with the theme of experimenting and fairytales, we present this narrative in the form of a fairytale; and, without further ado…
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".