Dataset for assessing the scope and nature of global stream daylighting practices
Bibliographic record
Abstract
This paper presents five publicly available datasets (I through V) of which two are interactive and visual tools (a Tableau Dashboard and an Interactive Map). These five datasets were extracted from 115 literature sources on the daylighting of streams that were published between 1992 and 2018. Dataset I consist of 19 variables that combine two types of data extracted from these sources: ten manifest variables (indisputable, obvious, factual) and nine variables extracted from the sources’ latent content (indirect, hence, based on careful reading of the sources’ contents). Manifest variables include, among others, authors’ names and affiliations, authorship location, and publication year. Latent variables include primarily the literature sources’ underlying themes and their sub-themes (sub-categories), the daylighting case studies/projects discussed, and the geographic coverage or scope addressed in the literature sources. Datasets II identifies 16 literature sources that delve into the climate change adaptation and/or mitigation theme and reveal how it was tackled vis-à-vis the other themes/sub-themes. Dataset III identifies and provides detailed information on the 145 different stream daylighting case studies/projects mentioned in the literature's sources, such as each project's location, daylighted length, completion date, cost, and type of treatment. Dataset IV is a Tableau Dashboard that offers interactive analytical querying in the form of relational analyses and data visualization while Dataset V is an Interactive Map created in Google My Map that maps the 145 stream daylighting case studies/projects mentioned in the literature sources over and provides a synopsis on each based on the literature's contents. The combination of these five datasets and their diversity in type and presentation yields a comprehensive, global, and unique repository of information on the daylighting of urban streams for all types of audiences (academic, professional, and laypeople).
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".