Understanding the breadth and depth of long-term ecological data collection in Canada and the United States
Bibliographic record
Abstract
While several formal long-term ecological monitoring or research networks have been established, efforts are undertaken by many different organizations, leaving little understanding on the breadth of taxa captured or the length of data being collected across Canada and the U.S. We compiled information on long-term ecological efforts using known formal networks, conference abstracts, a literature search, an online survey, and federal and state agency data records. Of the 590 efforts identified, 175 were in Canada and 418 were in the U.S. Most (62%) were species-focused, with only 38% taking a whole ecosystem approach. Of the 365 efforts that were on species, 23%, 23%, 21%, 17%, and 15% focused on birds, fish, mammals, herpetofauna, and plants, respectively. Efforts averaged 21 (max = 157) years in length. Qualitatively, efforts seemed to be well distributed across the U.S., with some concentration of effort in coastal areas and the Great Lakes region. While singular taxonomic efforts were sparse across central Canada, regional and continental efforts provided vast coverage for some fauna such as birds, and coastal waters were well monitored by several agencies. Our work supports the idea that long-term ecological efforts have clear value given how numerous they are.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".