Bibliographic record
Abstract
This manuscript by Skov et al. presents a time-series of gaseous elemental mercury (GEM) concentrations at Villum Research Station (Station Nord), Greenland from 1999 to 2002 and 2009 to 2017.Alert (Canada), Station Nord, and Ny-Ålesund (Spitzbergen) are presently the only three long-term monitoring stations for GEM in the Arctic.As such, the data presented here are extremely valuable and I would like to acknowledge the authors for their work and dedication.That being said, I do not find the interpretation of the data convincing, mostly due to a confusing Material & Methods section (see suggestions below).The Results and Discussion section is also difficult to follow; I wasn't always sure whether the authors were referring to the annual or seasonal trend.Reorganizing the discussion per season could help.Finally, I think there is a lack of C1 ACPD Interactive comment Printer-friendly version Discussion papersufficient references, especially recent ones.Below are some detailed comments and suggestions that will hopefully help the authors to improve their manuscript.Measurements section 1. Line 89: "several generations of the instrument have been used (A, B, and X version)".Could you add somewhere (in the text and/or on Figure 3) the dates at which the Tekran instrument was changed?Given the 20% intercomparison uncertainty between two instruments (Slemr et al., 2015), that should I think be taken into account when performing a trend analysis.The winter trend seems driven by the high value in 2000 and the low value in 2017.Does it coincide with a different instrument being used?According to Angot et al. (2016), you used a Tekran 2537A at least from 2011 to 2015.According to Kamp et al. (2018), you used a Tekran 2537X in spring 2016.When did you switch?Did you measure GEM concentrations with the two instruments for a certain period of time in order to evaluate the intercomparison uncertainty?The lack of information casts doubts on the trend analysis.GEM trend analysis is of utmost importance for the effectiveness evaluation of the Minamata Convention.However, potential implication of the use of multiple instruments for GEM trend analysis is somewhat overlooked by the community.A discussion on the matter could strengthen the conclusions of the manuscript.2. What is the time resolution of the GEM measurements?5 or 15 minutes?Did you use the 5/15 min data for the trend analysis or hourly means/medians, or annual averages?Trend analysisHow did you perform the trend analysis?Please describe the method in the Material and Methods Section.It seems that you are simply using the regression line.It is of common practice to use the Sen's slope and Mann-Kendall test for trend analysis (e.g., Berg et al., 2013;Cole and Steffen, 2010;Martin et al., 2017).Again, the lack of information casts doubts on the trend analysis.
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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 | 0.006 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.531 | 0.282 |
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".