Methods for biogeochemical studies of sea ice: The state of the art, caveats, and recommendations
Bibliographic record
Abstract
Abstract Over the past two decades, with recognition that the ocean’s sea-ice cover is neither insensitive to climate change nor a barrier to light and matter, research in sea-ice biogeochemistry has accelerated significantly, bringing together a multi-disciplinary community from a variety of fields. This disciplinary diversity has contributed a wide range of methodological techniques and approaches to sea-ice studies, complicating comparisons of the results and the development of conceptual and numerical models to describe the important biogeochemical processes occurring in sea ice. Almost all chemical elements, compounds, and biogeochemical processes relevant to Earth system science are measured in sea ice, with published methods available for determining biomass, pigments, net community production, primary production, bacterial activity, macronutrients, numerous natural and anthropogenic organic compounds, trace elements, reactive and inert gases, sulfur species, the carbon dioxide system parameters, stable isotopes, and water-ice-atmosphere fluxes of gases, liquids, and solids. For most of these measurements, multiple sampling and processing techniques are available, but to date there has been little intercomparison or intercalibration between methods. In addition, researchers collect different types of ancillary data and document their samples differently, further confounding comparisons between studies. These problems are compounded by the heterogeneity of sea ice, in which even adjacent cores can have dramatically different biogeochemical compositions. We recommend that, in future investigations, researchers design their programs based on nested sampling patterns, collect a core suite of ancillary measurements, and employ a standard approach for sample identification and documentation. In addition, intercalibration exercises are most critically needed for measurements of biomass, primary production, nutrients, dissolved and particulate organic matter (including exopolymers), the CO2 system, air-ice gas fluxes, and aerosol production. We also encourage the development of in situ probes robust enough for long-term deployment in sea ice, particularly for biological parameters, the CO2 system, and other gases.
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 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 | 0.102 | 0.315 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.012 | 0.006 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".