Building Cross-Site and Cross-Network collaborations in critical zone science
Bibliographic record
Abstract
The critical zone (CZ) includes natural and anthropogenic environments, where life, energy and matter cycles combine in complex interactions in time and space. Critical zone observatories (CZOs) have been established around the world, yet their limitations in space and duration of observations, as well as the oft-existing dominant disciplinary research field(s) of each CZO may limit the transferability of the local knowledge to other settings or hinder integrative CZ understanding. In this regard, this review advocates for cross-site cross-network collaborations in CZ sciences. We posit that this type of collaboration is becoming indispensable for understanding past trends and future trajectories of the CZ, in the context of fast-developing and widespread environmental changes. Aided by a series of cyberseminars and a community survey, we highlight some of the existing cross-site initiatives, tools and techniques, and the cross-cutting science questions that could benefit from such cross-network syntheses, in various types of CZ settings (montane, alpine, arctic, managed and agricultural environments, lakes, wetlands, streams, landscapes disturbed by drought and/or wildfire, etc.). This review also identifies and discusses the major and legitimate concerns and obstacles for a collaborative CZ approach, including data harmonization and integration of social sciences, and proposes tentative ways forward.
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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 | 0.082 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.016 | 0.029 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".