Recommended practice for reporting experimental data produced from studies on corrosion of steel in cementitious systems
Bibliographic record
Abstract
Experience has shown that many aspects of experimental design for studying steel corrosion in cementitious systems may significantly influence the obtained results. In the absence of standardized methods to study steel corrosion in concrete, researchers usually define their own test setups, which partially explains the large scatter and uncertainty in the aggregated published data. When the details of these setups are not provided adequately, experimental results cannot be interpreted in a wider context. Unfortunately, many scientific publications lack important experimental details. Therefore, this paper aims at improving the quality of reported experimental details, observations, and data in scientific publications, and raising awareness for relevant issues to improve the quality of research in the field. To this end, this paper provides a list of experimental details that have been found important by many decades of research, and which are, thus, recommended to be considered in conducting and reporting laboratory studies involving corrosion of steel embedded in cementitious systems. Finally, we propose a checklist for reporting experimental data in scientific publications.
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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 | 0.419 | 0.686 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.045 | 0.033 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.012 | 0.010 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.015 | 0.022 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".