Paleomagnetism of Portuguese stalagmites: advances and perspectives
Bibliographic record
Abstract
Speleothems are secondary mineral deposits formed in caves and may represent excellent recorders of variations of the Earth’s magnetic field. Magnetic studies of speleothems provide two important forms of data: i) continuous and high-resolution records of short-term variations of the geomagnetic field (GMF), and conversely the use of paleomagnetism as a dating tool of speleothems; and ii) high-resolution records of climate variability by linking rock magnetic properties to climate and environmental forcing parameters acting on soils. However, the use of speleothem magnetism as a dating tool and paleo-climate archive still requires a great deal of maturation before being comparable to traditional techniques. Here we present a review of the paleomagnetic records of speleothems from Portugal obtained during the last five years and propose a new approach to date speleothem based on paleomagnetic data. We also present preliminary results and future investigations in the framework of an ongoing research project (Speleothems as magnetic, environmental and paleo-fire archives; ref. PTDC/CTA-GEO/0125/2021) and a bilateral exploratory MIT-FCT project (Analyzing environmental and climate records in Portuguese speleothems using ultra-high sensitivity magnetic microscopy; ref. MIT-EXPL/ACC/0023/2021) both funded by the Foundation of Sciences and Technology (FCT) of Portugal.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".