Calculating Food Production in the Subsistence Harvest of Birds and Eggs + Supplementary Appendices S1 and S2
Bibliographic record
Abstract
Subsistence harvest studies use number-to-mass conversion factors (CFn-m) to transform numbers of animals harvested into food production (CFn-m = body mass × recovery rate; where recovery rate is the percentage of the body mass represented by the processed carcass). Also, if egg harvest was reported as volume (e.g., a bucket), volume-to-number conversion factors (CFv-n) are needed to calculate the number of eggs taken. Conversion factors (CF) for subsistence harvest of birds and eggs have been based on unclear assumptions. We calculated a mean recovery rate (65%) by weighing and processing wild birds, compiled data on bird and egg mass, developed an egg CFv-n equation, and presented CF for 88 bird species, 13 subspecies or populations, and 25 species categories likely to be harvested in Alaska. We also made recommendations on how to apply and adjust CF according to study objectives. We recommend that subsistence harvest studies (1) collect egg harvest data as egg numbers (not volume); (2) clearly explain considerations and assumptions used in CF; (3) report recovery rates and mass of birds and eggs; and (4) cite original sources when referring to CF from previous studies. Attention to these points of method will improve the accuracy of food production estimates and the validity of food production comparisons across time and geographic areas.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.319 | 0.065 |
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".