Bibliographic record
Abstract
Although nearly 50 years have passed since P.B. Sears introduced pollen analysis to North America, it remains an occult art. Dramatic improvements in sampling and analytic techniques continue to be limited by intractable problems of differential production, dispersal, ballistics, sedimentation, and preservation. It is a basic tenet of pollen stratigraphy that the data set, consisting primarily of microfossils preserved in sediments, is better than anything we have yet been able to do with it. Basic agreement between late- and postglacial pollen records has been confirmed wherever the method has been applied. Quantitative sampling techniques, sample preparation, and analytic procedures, together with multiple radiocarbon dates, permits calculation of sedimentation rates and absolute pollen influx. Of approximately 300 sediment cores from northeastern North America, fewer than 30 have more than 3 radiocarbon determinations from which least squares power curve regressions can be reliably calculated in the determination of sedimentation rates. Analogy with modern environments represented by surface pollen spectra is limited by an insufficient number of samples of uniform quality to characterize a vegetational mosaic covering 40 degrees of latitude (40-80°N) and longitude (60-100°W). The present surface pollen data bank includes about 700 samples, unevenly spaced and of uneven quality, permitting a grid resolution of no better than 10,000 km2.
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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".