Arctic Borderlands Ecological Knowledge Cooperative: can local knowledge inform caribou management?
Bibliographic record
Abstract
While quantitative analyses have traditionally been used to measure overall caribou herd health, qualitative observational data can also provide timely information that reflects what people on the land are observing. The Arctic Borderlands Ecological Knowledge Co-op (ABEKC) monitors ecological change in the range of the Porcupine Caribou Herd (PCH). The community-based monitoring component of the Co-op’s mandate involves the gathering of local knowledge through interviews with local experts in a number of communities.We analyzed the responses to interviews collected during 2000–2007 related to caribou availability, harvest success, meeting needs and caribou health during fall and spring. Interviews revealed 1) caribou greater availability during the survey period, 2) an increasing trend in the proportion of harvesters that met their needs 3) no trend in animals harvested or proportion of successful hunters and 4) improving overall caribou health throughout the period.There was no population estimate for the herd between 2001 and 2010. In 2001, 123,000 caribou were estimated in the herd. Based on an estimated 178,000 in 1989, a declining trend of ~ 3% annually occurred at least until 2001. In the interim agencies and boards feared the herd continued to decline and worked towards and finalized a Harvest Management Plan for the herd. In contrast, from the Co-op interviews all indications suggested improving herd conditions throughout most of the decade. A successful survey in 2010 determined the herd had grown to 169,000 animals. We conclude that the community-based interviews provided a valid, unique information source to better understand caribou ecology and express community perceptions of overall herd status and could provide a valuable contribution to management decision making. We recommend that ABEKC results become standard input into Porcupine Caribou harvest management decisions and serve as a model of integrating community based monitoring data into resource management decision making throughout the north.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; both teacher heads agree on what is shown here.
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".