It Takes a Village: Using a Crowdsourced Approach to Investigate Organic Matter Composition in Global Rivers Through the Lens of Ecological Theory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Though community-based scientific approaches are becoming more common, many scientific efforts are conducted by small groups of researchers that together develop a concept, analyze data, and interpret results that ultimately translate into a publication. Here, we present a community effort that breaks these traditional boundaries of the publication process by engaging the scientific community from initial hypothesis generation to final publication. We leverage community-generated data from the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) consortium to study organic matter composition through the lens of ecological theory. This community endeavor will use a suite of paired physical and chemical datasets collected from 97 river corridors across the globe. With our first step aimed at ideation, we engaged a community of scientists from over 20 countries and 60 institutions, spanning disciplines and career stages by holding a virtual workshop (April 2021). In the workshop, participants generated content for questions, hypotheses, and proposed analyses based on the WHONDRS dataset. These ideation efforts resulted in several narratives investigating different questions led by different teams, which will be the basis for research articles in a Frontiers in Water collection. Currently, the community is collectively analyzing, interpreting, and synthesizing these data that will result in six crowdsourced articles using a single, existing WHONDRS dataset. The use of a shared dataset across articles not only lowers barriers for broad participation by not requiring generation of new data, but also provides unique opportunities for emergent learning by connecting outcomes across studies. Here we will explain methods used to enable this community endeavor aimed to promote a greater diversity of thinking on river corridor biogeochemistry through crowdsourced science.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it