Application of a sequential partial extraction procedure to investigate uranium, copper, zinc, iron and manganese partitioning in recent lake, stream and bog sediments, northern Saskatchewan / by Douglas Andrew Warren Lehto. --
Bibliographic record
Abstract
Sequential partial extractions show that partitioning of uranium, \ncopper, zinc, iron and manganese into lake, stream and bog sediments \nare affected by the type and abundance of component fractions present \nin sediments and by the physico-chemical conditions of the superjacent \nwaters. The water pH influences the concentration of uranium retained \nby organic matter as well as the relative proportion partitioned into \nthe amorphous iron hydroxide fraction and the humic and fulvic acid \ncomponents of the organic matter fraction. Copper partitioning is \ncontrolled by the percent carbon content of sediments which influences \nthe concentration of metal retained in the organic matter fraction. \nThe amount of copper retained by other component fractions is determined \nby their relative abundance in sediments. The Eh-pH conditions \nof the superjacent waters control the solubilities of iron, manganese \nand zinc thereby affecting the availability and sorption of these \nmetals into the organic matter and inorganic hydroxide fractions of \nsediment. Metal partitioning characteristics and physico-chemical \nfactors which influence metal partitioning should be considered when \nusing lake, stream and bog sediments in geochemical exploration.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".