Sources and accumulation of sediment and particulate organic carbon in a subarctic fjard estuary: <sup>210</sup>Pb, <sup>137</sup>Cs, and δ<sup>13</sup>C records from Lake Melville, Labrador
Bibliographic record
Abstract
The sources and distribution of sediment and particulate organic carbon (OC) to Lake Melville, Labrador, were characterized to better understand impacts from climate and hydrological changes to the system. Mass accumulation rates (MARs) across the Lake Melville System (LMS) were established from 15 sediment cores collected in 2013 and 2014 by fitting excess 210Pb (210Pbex) profiles to a two-layer advection–diffusion model. MARs, validated using 137Cs, varied between 0.04 and 0.41 g cm−2 a−1, and overall decreased with increasing distance from the Churchill River, which drains into Goose Bay, a western extension of Lake Melville. The Churchill River is the greatest source of sediment to the system, but surprisingly, MARs were greatest in western Lake Melville rather than Goose Bay, reflecting the contribution of fine material carried eastward in the Churchill River plume and inputs from nearby tributaries. A comparison of 137Cs and 210Pbex inventories to expected atmospheric fallout (1.5 and 23.6 disintegrations per minute (dpm) cm−2, respectively) in sediment across the LMS suggests particles are largely sourced from the watershed. In eastern Lake Melville, elevated 210Pbex inventories and marine OC point to particle scavenging of dissolved 210Pb from inflowing marine water. A transient tracer mixing model was used to determine the depth in each core where >90% of sediment was deposited before and after hydroelectric development at Churchill Falls (1970) and applied to down-core profiles of OC and organic carbon isotopes (δ13Corg). We observed a significant increase of terrestrial OC to Lake Melville post 1970, which we interpret as change from climate or hydrology of the Churchill River.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".