Estimating past and future dinosaur skeletal abundances in Dinosaur Provincial Park, Alberta, Canada
Bibliographic record
Abstract
Some 353 isolated skulls and partial to complete skeletons with known locations have been collected in ∼100 years from the 80 km2 of badlands in Dinosaur Provincial Park (DPP), Alberta, Canada. We wanted to estimate how many skeletons were lost to erosion before collection began and how many await discovery. Within the boundaries of DPP, a volume of rock 145 m thick between the surface of the down-cutting Red Deer River and the capping prairie was subdivided into 5 m thick slabs using digital elevation data with an average horizontal spatial resolution of 19 m and a vertical resolution of 1 m. The exposed surface area of each slab was calculated. Dinosaur fossil localities were determined with high-precision GPS surveys. The number of dinosaurs collected from the surface of a 5 m slab was divided by the product of the exposed area and an estimated erosional thickness of 80 cm to give a volume density of dinosaur fossils. Multiplying the volumes of rock lost from each layer by the dinosaur densities for each layer, the numbers of skeletons lost was determined. Estimates of the numbers of raisins in two loaves of raisin bread were made using a limited number of slices as a test of the method. Of the original volume of DPP, 6.58 km3 (60%) has eroded away, taking with it a mean number of 6310 hadrosaurs, 1640 ceratopsians, 1030 ankylosaurs, and 1600 theropods. The 5.02 km3 (40%) of rock remaining in the park can be expected to produce more dinosaur fossils of similar quality, with mean values of 6700 hadrosaurs, 1700 ceratopsians, 1010 ankylosaurs, and 1720 theropods. These estimates are minima as the estimation process excluded bone beds, the plethora of isolated bones littering the land surface of DPP, and the 100+ skulls and skeletons from the region that lack locality information.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".