Supplementary material to "Teaching hydrological modelling: Illustrating model structureuncertainty with a ready-to-use teaching module"
Bibliographic record
Abstract
In order to get a clearer picture on if and how uncertainty is currently taught to students in the field of Earth-and Environmental Sciences a quick survey on "Teaching Uncertainty in Hydrological Modelling" was conducted via the survey software surveymonkey.com.The main questions we wanted to answer were:1. How commonly is uncertainty in hydrological modelling part of the teaching curriculum in water resources (related) courses?2. Are data uncertainty, parameter uncertainty and model structure uncertainty equally often covered in the curriculum? 3. Which tools are being used to teach hydrological modelling and, specifically, how often are modelling exercises part of the curriculum?We wanted to design the survey as short as possible in order to get many participants to take the time to answer it.We hoped that a large number of participants would allow a broad general overview on the topic.The survey was distributed via twitter, different e-mail lists and by explicitly searching for and contacting different hydrology institutes and water research related faculties.Additionally, a flyer to the survey was passed out during AGU 2019.We were able to collect 101 answers in approximately 6 months (the survey was open between 18.09.2019and 06.03.2020).The survey had between eight and eleven questions depending on the path the participants took through the survey (see Figure S1) and took approximately three minutes to answer.In the following section the survey questions and answer possibilities are listed with the number of responses in blue.Note that different paths through the survey were possible depending on which answers were given, resulting in consecutive questions occasionally having a different number of respondents.Due to the way the survey tool works, certain questions needed to be duplicated to let them be part of different paths.The different survey paths and their corresponding questions can be seen in Figure S1 displaying a flow diagram of the survey with its introductory text.
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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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.858 | 0.393 |
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".