Bibliographic record
Abstract
Humpback whale line transect data ================================= Data used in paper "Mixture models for distance sampling detection functions" by David L. Miller and Len Thomas. Taken from Williams and Thomas (2007) and were provided by Rob Williams. Data are line transect observations of 76 humpback whales (Megaptera novaeangliae) in British Columbia. Survey details ============== Survey design and protocol are detailed in Thomas et al 2007. Multi-species small boat surveys were conducted along the coastline of British Columbia, Canada during the summers of 2004 and 2005. Data format =========== Comma separated value file with one row for each of the 76 observations, each with the following 3 columns: distance : Exact perpendicular distances to observed humpbacks in metres. object : Unique observation identifier. detected : Column of 1s indicated that the individual was observed (needed for analysis software). References ========== Williams, R, and L Thomas. Distribution and Abundance of Marine Mammals in the Coastal Waters of British Columbia, Canada. Journal of Cetacean Research and Management 9, no. 1 (2007): 15. Thomas, L, R Williams, and D Sandilands. Designing Line Transect Surveys for Complex Survey Regions. Journal of Cetacean Research and Management 9, no. 1 (2007): 1.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.076 | 0.042 |
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".