Bibliographic record
Abstract
Fossil plant remains are preserved within the deposits of the Eocene Aspelintoppen Formation on Svalbard. These sediments form the youngest continental deposits of early Paleogene age. The Aspelintoppen Formation sediments represent crevasse splay, backswamp and ephemeral lake deposits that represent a broad lowland floodplain that was subject to frequent flooding. The forests grew at a palaeolatitude of 75°N. New collections (1032 specimens) ofthe Aspelintoppen Formation flora are dominated by angiosperms including the Fagaceae? (Ushia olafsenii), Betulaceae (Corylites and Craspedodromophyllum), Hamamelidaceae (Platimelis pterospermoides), Platanaceae (Platimeliphyllum and Platanus), Ulmaceae (Ulmites ulmifolius), Trochodendraceae (Zizyphoides flabella), Cercidiphyllaceae (Trochodendroides), Juglandaceae (Juglans laurifolia) and Hippocastinaceae (Aesculus longipedunculus). In addition, conifer fossils include Metasequoia shoots and cones, as well as Thuja shoots. Fern fronds of Osmunda and Coniopteris are present, along with the horsetail Equisetum. The Aspelintoppen Formation vegetation grew locally on the floodplain with angiosperms dominating the riparian environment and a mixed angiosperm Metasequoia-dominated flora in the backswamp environment, with Equisetum and ferns occupying the margins of ephemeral lakes and post-disturbance environments. The Aspelintoppen Formation flora is similar in composition and ecology to other early Paleogene Arctic floras from the Canadian Arctic, Greenland, Alaska and north-east Russia, showing that Polar Broadleaved Deciduous Forests were a dominant part of the Arctic environment. Palaeoclimate estimates were derived from the 22 angiosperm morphotypes using both physiognomic and nearest living relative methods. CLAMP results are considered to be the most reliable and indicate that the Eocene climate of Svalbard was temperate with a mean annual temperature of 11.6°C, a warm month mean of 18.rC and a cold month mean of 4.5°C. Precipitation estimates indicate high levels of precipitation with growing season precipitation estimates from 320 to 1531mm, and a strong wet or dry seasonal signal with 356 to 656mm precipitation for the three wettest months and 112 to 247mm for the three driest months. These estimates support sedimentary evidence that Eocene Arctic environments were seasonally warm and wet.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".