Understanding the Middle Miocene Climatic Optimum: Evaluation of Deuterium Values (δD) Related to Precipitation and Temperature
Bibliographic record
Abstract
The Middle Miocene Climate Optimum was a unique warming period in the Earth’s geologic history, when a high global mean annual temperature was accompanied by a relatively low global CO2 concentration. Hydrogen isotopic signals (specifically molecular δD, the ratio of deuterium to hydrogen) from lipids of fossils and sediments offer intrinsic insights into precipitation of ancient climates. Using samples collected from known Middle Miocene deposits, we measured δD of n-alkanes extracted from well-preserved plant and sediment samples from varying latitudes across the Northern Hemisphere, and then analyzed the data through a zonally averaged precipitation and evaporation climate model. The reduced latitudinal temperature gradient with warm polar regions during the Middle Miocene was also contrarily coupled with a small variance in latitudinal meteoric water composition and precipitation. With our latitudinally variant sample locations (ranging from 24°N in Xianfeng, China, to 74°N in Banks Island, Canada), we developed a one-dimensional model in which we assessed evaporation and precipitation gradients throughout the Northern Hemisphere. Ultimately, we used the latitudinal distribution of δD to better constrain the atmospheric conditions during the Middle Miocene Climatic Optimum.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".