Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
They just kept sending them into our meat grinder. We've killed sev-- eral hundred of them, but they just keep coming. -Major General F.L. Hagenbeck2 As of 2 March 2002, Operation Anaconda was the largest combat operation in Afghanistan of the War on Terrorism that began after the at-- tack on the World Trade Center and the Pentagon on 11 September 2001. Major General F.L. Hagenbeck, commander of the U.S. Army 10th Mountain Division, led the major effort to clean out remaining al-- Qaeda fighters and their Taliban al-- lies in the Shah-i-Khot Valley. The mission involved about 2,000 coali-- tion troops, including more than 900 Americans, 200 U.S. Special Forces and other troops, and 200 special operations troops from Australia, Canada, Denmark, Germany, France, Norway, New Zealand, and Afghan allies. Operation Anaconda began before dawn on 2 March 2002. The battle area occupied about 60 square miles. The terrain is rugged, and the peaks have many spurs and ridges. The base of the Shah-i-Khot Valley is approximately 8,500 feet in altitude. The surrounding mountain peaks rise to 11,000 to 12,000 feet. Only small juniper trees grow on the mountain slopes. The actual snow line began about 100 feet above the valley floor. Mountain villages include the ham-- lets of Sher Khan Khel, Babal Khel, Marzak, Kay Khel, and Noor Khel. On the day battle began, the valley floor was sprinkled with small patches of snow. Temperatures hov-- ered near 15 to 20 degrees Fahrenheit.3 The opposition forces were mostly non-Afghan al-Qaeda and Taliban members although the force also in-- cluded some Arabs, Chechens, Uzbeks, and Pakistanis. Scattered groups, numbering as many as 20 members, including some family members, holed up in a 3,000-year-- old complex of mountain tunnels, caves, and crannies. The terrorists, who had come to the valley villages six weeks before the battle began, took control; pru-- dently, most of the civilians left. One Afghan villager said the people were told, If you want to leave or stay it is up to you, but we're staying in those caves because they were ours in the holy war against Russia.4 The terrorists gave 700 sheep to the people of Shah-i-Khot for their troubles; others received bus fare. Predator drones and other CIA in-- telligence assets spotted the enemy assembling in groups south of Gardez, but rather than immediately attacking, U.S. Central Command (CENTCOM) let the terrorists gather to present a larger target. A small U.S. Special Forces detachment ac-- companied local Afghan commander Zia Lodin as his men entered the valley from the south and headed to Sirkankel to flush out suspected al-- Qaeda and Taliban forces.5 To the east and southeast of the combat area, Afghan generals Kamal Khan Zadran and Zakim Khan's units had responsibility for the pe-- rimeter. U.S. Special Forces teams were with each Afghan general to help coordinate operations. This noose of allied troops enclosed four specific combat zones. The two most significant zones were code-named Objectives Remington and Ginger. Reconnaissance forces slipped into the mountains a few days before the main attack was scheduled to begin on 27 February, but the operation was postponed 48 hours because of rainy, blustery weather. When the operation began, Zia ran into trouble. His 450-man unit was caught in a mortar barrage and pre-- vented from entering Sirkankel. Two of Zia's men were killed and 24 were wounded. Retreating under mortar and rocket fire, the Afghan column stumbled into a second ambush to the rear. U.S. Special Forces Chief Warrant Officer Stanley L. Harriman was killed. Most of Zia's trucks were destroyed, and his troops retreated to Gardez.6 The hole left by Zia's retreat had to be plugged. U.S. troops, who had been slated to block fleeing terrorists or hopscotch around the battle zone, were immediately dropped into the gap to await Zia's return. Elements of the U. …
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.046 | 0.003 |
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 it