Wildlife monitoring in Finland: online information for game administration, hunters, and the wider public
Bibliographic record
Abstract
Annual, nationwide monitoring of the grouse (hunted tetraonid birds) began in Finland at the beginning of the 1960s followed by systematic counts of mammal snow tracks in the late 1980s. The wildlife triangle scheme, started in 1989, gathers game monitoring data throughout the country. The system is based on a large network of triangles made up of 4 km transects (totaling 12 km per triangle) covering the entire country. The program involves an astonishing amount of fieldwork: about 10 000 km of transect line (about half of the established transects) is studied during every summer and winter count. The riistakolmiot.fi internet service was launched for the 2014 late-summer count. Via the internet, trained hunters can record their observations in a database and follow the progress of the count during the fieldwork period. In the public section of the website, anyone can view the results of ongoing counts. The internet service speeds up the collection of observations, simplifies the storing of data, and assists in preparing and sending the summary reports. Data provided by the wildlife triangle scheme are utilized by the European Union, the Ministry of Agriculture and Forestry, and other game administrative organizations, as well as hunting clubs and the general public. Annual results of the late-summer monitoring procedure are used immediately when deciding on the restrictions to the forthcoming grouse hunting season, which is due to begin just a few weeks later.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.027 |
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".