An ecologically sound and participatory monitoring network for pan‐Arctic seabirds
Bibliographic record
Abstract
In a warming Arctic, circumpolar long-term monitoring programs are key to advancing ecological knowledge and informing environmental policies. Calls for better involvement of Arctic peoples in all stages of the monitoring process are widespread, although such transformation of Arctic science is still in its infancy. Seabirds stand out as ecological sentinels of environmental changes, and priority has been given to implement the Circumpolar Seabird Monitoring Plan (CSMP). We assessed the representativeness of a pan-Arctic seabird monitoring network focused on the black-legged kittiwake (Rissa tridactyla) by comparing the distribution of environmental variables for all known versus monitored colonies. We found that with respect to its spatiotemporal coverage, this monitoring network does not fully embrace current and future environmental gradients. To improve the current scheme, we designed a method to identify colonies whose inclusion in the monitoring network will improve its ecological representativeness, limit logistical constraints, and improve involvement of Arctic peoples. We thereby highlight that inclusion of study sites in the Bering Sea, Siberia, western Russia, northern Norway, and southeastern Greenland could improve the current monitoring network and that their proximity to local populations might allow increased involvement of local communities. Our framework can be applied to improve existing monitoring networks in other ecoregions and sociological contexts.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".