Analysing the contribution of snow water equivalent to the terrestrial water storage over Canada
Bibliographic record
Abstract
An error has been identified in Tables 2 and 3 of the article “Analysing the contribution of snow water equivalent to the terrestrial water storage over Canada” (DOI: 10.1002/hyp.13625) (Bahrami A, Goïta K, Magagi R. (2020)), published in Wiley Online Library on 5 December 2019 in Hydrological Processes, 34:175–188 In Table 2, the bold values were incorrectly described as being the highest values. In fact, they were the insignificant Rs results. In the corrected table below, the insignificant values are now identified with an asterisk. The highest values are not indicated. In Table 3, a note to the table that described bold values and gray shading was incorrectly included. In the correct version of the table below it has been deleted. The corrected tables are as follows.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".