Bibliographic record
Abstract
Most laboratories aim to produce data of the highest quality. Trying to lower uncertainties to infinitesimal figures and push detection limits even lower are valid goals. However, is it possible to overachieve? Are old data still of good enough quality to be usable? In a geochemical context, the main goal of producing analytical results is to answer geological or environmental questions. Not all scientific problems require the same data quality. What is really required are data of adequate quality – i.e., ‘fit‐for‐purpose’– to ensure that the geological problem at hand can be solved. Furthermore, it is doubtful that uncertainties and reproducibilities associated with field sampling are better than those from laboratories. It is thus proposed that, as geoanalysts, we encourage data users (students, colleagues or referees) to ensure that their analytical results are of sufficient quality to solve the problem. However, authors have to demonstrate, through the use of reproducibility testing, reference and quality control materials, that the quality of their results is sufficient to solve the problem. Uncertainties and detection limits in publications should not only be evaluated with respect to a set value, such as 10%, but also with regard to the geological problem to be solved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".